| Income provided... | Formula to convert to annual | Formula to convert to weekly | Formula to convert to hourly |
|---|---|---|---|
| ... annually | = income | = income / working weeks | = weekly income / hours worked per week |
| ... monthly | = income x 12 | = (income x 12) / working weeks | = weekly income / hours worked per week |
| ... weekly | = income x working weeks | = income | = weekly income / hours worked per week |
| ... hourly | = income x hours per week x working weeks | = income x hours per week | = weekly income / hours worked per week |
Methodology
1 Participants and Design
[Insert Name of Report] contains data from two studies. The first of these studies was a nationally representative survey of 10155 [Workers?] conducted by Opinium Research between 25th November 2023 and the 21st December 2023.
To achieve a robust estimate of outsourced workers, the sample was weighted by age, gender, and education, region, and ethnicity. The ethnic minority sub-sample (1,435 respondents) was also weighted separately by age, gender, and region to ensure that findings related to ethnic minority adults were fully representative. Targets were estimated using data from the Labour Force Survey, the 2021 Census for England and Wales, and the Northern Ireland Census.
This sample had median age 42 (SD = 13.02). 51% of respondents identified as female, 48% as male, 0.14% as other, and 0.65% preferred not to identify a gender. 76% of respondents identified as ‘English / Welsh / Scottish / Northern Irish / British’ (see Section 8.3 for a detailed breakdown of ethnicity).
A follow-up survey of Outsourced workers (as defined in Section 2.1) was conducted by Opinium Research between 19th April to the 16th of May 2024 with a total sample of 1814. The purpose of this study was to further probe the experiences of outsourced workers and to understand the impact of outsourcing on their work and lives (see Section 2.2).
Soft quotas on age, gender, and region were implemented to ensure broad representativeness, and the final data was weighted to targets based on age, gender, education, region, and ethnicity. The targets were based on the weighted data from study 1. The survey population had a mean Age of 38.9 (SD = 13.0). 42.4% Female and 65.5% White British. A small proportion of respondents had previously participated in study 1 and met the outsourced criteria (5%).
Both surveys were administered online.
An initial pilot study aimed to refine the diagnostic questions used to identify outsourced workers, ensuring they aligned with JRF’s initial definition and could be accurately answered by survey respondents. The diagnostic questions and feedback follow-ups were run on Opinium’s political omnibus, a nationally and politically representative sample of 2,055 UK adults between 30 August and 1 September 2023. The questions were filtered to those in work, resulting in a total of 1,200 respondents. Data from this pilot study is not reported here.
[POTENTIALLY ADD A TABLE HERE WITH CROSSTABS FOR THE TWO SAMPLES OR TABBED VISUALISATIONS]
2 Measures
2.1 Study 1- Nationally representative survey
The survey covered personal demographics, employment demographics (e.g. occupation, hours worked, pay), and the outsourced diagnostic questions. The main objectives were to ensure an accurate estimate of the size and demographic makeup of the outsourced population, and to analyse the data alongside the Labour Force Survey (LFS). [MORGAN - WHAT EXACTLY WAS INTENDED TO BE COMPARABLE? SPECIFICALLY WHICH QUESTIONS HAVE BEEN REPLICATED FROM TLFS]
Comparability to the LFS posed challenges, primarily because the LFS is conducted face-to-face, with interviewers playing a significant role in ensuring the accuracy of data and respondents’ understanding of questions. However, as the Transformed Labour Force Survey (TLFS)– an online first version of the survey set to replace the LFS— was underway, where possible we used the TLFS versions. While question wording is still under review, this was deemed the best approach, as some TLFS waves had already taken place and findings on comparability to LFS [MORGAN - CITATION].
2.1.1 Income calculations ([perhaps more detailed than necessary in this section])
Respondents could choose how they provided information about their income. Firstly, they could choose the payment period for which to express their income from the following options:
- Annually / per year
- Monthly
- Weekly
- Hourly
Secondly, they could choose either an ‘open’ form of reporting or a ‘closed’ form. The open form required respondents to type in their pay for the payment period they chose. The closed form required respondents to select which income bracket their pay belonged to from a list of options.
The annual options were:
- Less than £5,600 a year
- £5,600 up to £11,200
- £11,201 up to £16,800
- £16,801 up to £22,400
- £22,401 up to £28,000
- £28,001 up to £33,600
- £33,601 up to £39,200
- £39,201 up to £44,800
- £44,801 up to £50,400
- £50,401 up to £56,000
- Over £56,000 a year
- Prefer not to say
The monthly options were:
- Less than £470 a month
- £470 up to £940
- £941 up to £1,410
- £1,411 up to £1,880
- £1,881 up to £2,350
- £2,351 up to £2,820
- £2,821 up to £3,290
- £3,291 up to £3,760
- £3,761 up to £4,230
- £4,231 up to £4,700
- Over £4,700 a month
- Prefer not to say
The weekly options were:
- Less than £110 a week
- £110 up to £220
- £221 up to £330
- £331 up to £440
- £441 up to £550
- £551 up to £660
- £661 up to £770
- £771 up to £880
- £881 up to £990
- £991 up to £1,100
- Over £1,100 a week
- Prefer not to say
The hourly options were:
- Less than £8.91 an hour
- £8.91 up to £10.00
- £10.01 up to £12.00
- £12.01 up to £14.00
- £14.01 up to £16.00
- £16.01 up to £18.00
- £18.01 up to £20.00
- £20.01 up to £22.00
- £22.01 up to £24.00
- £24.01 up to £27.00
- £27.01 up to £30.00
- Over £30.00 an hour
- Prefer not to say
7499 respondents answered using the open method. 1445 respondents answered using the closed method. 1211 did not answer either.
We equivalised respondents’ income across the reporting options in two steps. Firstly, we converted closed income responses to continuous numeric values by taking the midpoint of the income brackets, or the value of the “less than” and “over” values. For example, a closed response of “£5,600 up to £11,200” would be converted to £8400; and a closed response of “Less than £5,600 a year” would be converted to £5600. These converted closed responses were combined with the open responses to produce a single continous income variable across payment periods.
Next, we expressed all respondents’ income in annual, weekly, and hourly periods. To do this we made an assumption about the number of working weeks in a year based on the minimum holiday entitlement of 28 days. We calculated the total number of weeks in a year as 365 / 7 = 52.14, the total number of non-working weeks as 28 / 5 = 5.6, and thus the total number of working weeks as 52.14 - 5.6 = 46.54.
With this figure and the number of hours worked per week, we could convert incomes provided in one payment period to another. The table below shows how this was achieved.
[ADD OUTLIER EXCLUSION CRITERIA]
2.2 Study 2 - Outsourced workers survey
In the follow up survey of outsourced workers the data focuses on workers experiences and perceptions of outsourced work. The dataset is large containing 214 variables. Analysis of all the variables was beyond the scope of the report thus we focus on a subset of the data pertaining to outsourced workers experiences of rights violations, discrimination, job clarity, benefits and drawbacks of outsourced work and potential improvements to their work arrangements.
In the process of data cleaning we set hours per week to NA for participants who gave an impossible number of work hours per week (e.g. \(\ge\) 168, N=11). Relatedly we construct variables to determine hourly, weekly, monthly and annual pay as in study 1 and flag outlier responses. Through this method 11.22% (183) participants were dropped from all subsequent analysis leaving a final sample of 1631 participants. We also determine whether the participant is low paid using the method from study 1.
A data dictionary is available from the Github Repository associated with this project along with all code used to produce the analyses.
3 Analysis - Study 1
3.1 Defining outsourcing
Workers were defined as outsourced based on responses to a set of diagnostic questions. Three questions asked respondents directly about whether they considered themselves outsourced and/or agency workers.
The first of these questions asked respondents to indicate directly whether they considered themselves outsourced by selecting one of the following options:
- I am sure I’m an outsourced worker
- I think I might be an outsourced worker
- I am not an outsourced worker
The second question asked respondents to indicate whether they considered themselves an agency worker by selecting from three options. For respondents who responded 1 or 2 to question 1, the options were:
- I am sure that I’m also an agency worker
- I think I might also be an agency worker
- I am not an agency worker
For respondents who responded 3 to question 1, the options were:
- I am sure that I’m an agency worker
- I think I might be an agency worker
- I am not an agency worker
Respondents were also asked whether the work they do was long- or short-term by selecting one of:
- I’m hired to do work which an organisation needs doing on a long-term or ongoing basis.
- I’m hired to do work which an organisation needs doing on a short-term or temporary basis.
- Other (please specify)
Finally, respondents were asked about aspects of their work that might indicate that the work they do is outsourced work. Respondents were asked: “Please read each of the following statements and tell us whether or not they are true for you and your work.” The statements were:
- I am paid by one organisation but I do work for a different organisation.
- The organisation I’m paid by is a ‘third party’ organisation which other organisations hire to do work for them, rather than doing that w [FIND QUESTION IN DATA DICT]
- My employer / agency provides people to do work for other organisations (i.e. they might provide people to do cleaning, security, administratio [FIND QUESTION IN DATA DICT]
- On a day-to-day basis, I’m paid by one organisation but I get given tasks or instructions by people who are paid by a different organisation.
- I am paid by one organisation, but I work in a space which has the logo or branding of a different organisation.
- I wear a uniform which has the logo or branding of my employer / agency, and which marks me out as being paid by a different organisation to so [FIND QUESTION IN DATA DICT]
Workers were categorised into three mutually exclusive sub groups based on their responses to the above questions.
A respondent was categorised as ‘clearly outsourced’ if they responded ‘I am sure I’m an outsourced worker’ or ‘I think I might be an outsourced worker’ and ‘I’m hired to do work which an organisation needs doing on a long-term or ongoing basis.’.
A respondent was categorised as ‘likely agency’ if they responded ‘I am sure that I’m an agency worker’ and ‘I’m hired to do work which an organisation needs doing on a long-term or ongoing basis’, excluding those people who are already defined as being ‘clearly outsourced’.
A respondent was categorised as belonging to the ‘high indicators’ group if they responded TRUE to five or six [CAN THIS BE EXPRESSED AS \(\ge\) 5?] of the outsourcing indicators, as well as responding ‘I’m hired to do work which an organisation needs doing on a long-term or ongoing basis’, excluding those people who were already defined as ‘clearly outsourced’ or ‘likely agency’.
Together, these three sub groups form the classification of ‘outsourced workers’ considered in this report. Throughout the report, the term ‘outsourced’ refers to workers across the three sub groups. In places, analysis considers the three sub groups separately, in which case the groups will be referred to by name as ‘clearly outsourced’, ‘likely agency’, or ‘high indicators’.
3.2 Defining low pay
A ‘low pay’ binary variable was created by implementing an income threshold below which respondents were considered to be on a relatively low income. In line with with the Organisation for Economic Co-operation and Development, we set the threshold at two-thirds median weekly income. The two-thirds threshold was based on the weekly median income for respondents’ region to account for regional variations in earnings.
Regional weekly median income values were drawn from the Annual Survey of Hours and Earnings (2023 provisional edition). Respondents whose reported weekly income was less than or equal to two-thirds of the median weekly income in their region were assigned to the ‘low pay’ group, while those whose reported weekly income was greater than two-thirds of the median weekly income in their region were assigned to the ‘not low pay’ group.
3.3 Aggregating ethnicity
For reference, the table below provides a disambiguation of how ethnicities have been grouped in this analysis.
For analyses using the disaggregated (survey) categories with 21 levels, the reference category is “English / Welsh / Scottish / Northern Irish / British”.
For analyses using the aggregated categories with 9 levels, the reference category is “White British”
For analyses using teh aggregated categories with 4 levels, the reference category is “White”.
| Ethnicity: Survey (21 levels) | Ethnicity: Aggregated (9 levels) | Ethnicity: Binary (4 levels) |
|---|---|---|
| English / Welsh / Scottish / Northern Irish / British | White British | White |
| Irish | White other | White |
| Gypsy or Irish Traveller | White other | White |
| Roma | White other | White |
| Any other White background | White other | White |
| White and Black Caribbean | Mixed/Multiple ethnic group | Non-White |
| White and Black African | Mixed/Multiple ethnic group | Non-White |
| White and Asian | Mixed/Multiple ethnic group | Non-White |
| Any other Mixed / Multiple ethnic background | Mixed/Multiple ethnic group | Non-White |
| Indian | Asian/Asian British | Non-White |
| Pakistani | Asian/Asian British | Non-White |
| Bangladeshi | Asian/Asian British | Non-White |
| Chinese | Asian/Asian British | Non-White |
| Any other Asian background | Asian/Asian British | Non-White |
| African | Black/African/Caribbean/Black British | Non-White |
| Caribbean | Black/African/Caribbean/Black British | Non-White |
| Any other Black, Black British, or Caribbean background | Black/African/Caribbean/Black British | Non-White |
| Arab | Arab/British Arab | Non-White |
| Any other ethnic group | Other ethnic group | Non-White |
| Don’t think of myself as any of these | Don't think of myself as any of these | Don't think of myself as any of these |
| Prefer not to say | Prefer not to say | Prefer not to say |
| NA | NA | NA |
3.4 Models
In this section we describe the statistical models used in the report. In all models we applied survey weights so that the estimates can be considered representative of employees nationally.
3.4.1 Outsourced pay gap
To investigate the pay gap been outsourced and non-outsourced workers we constructed a linear regression model predicting annual and weekly income (in separate models) from outsourcing membership. We included other variables in the model to account for their potential influence on income. The full regression model can be expressed as:
\[ Income = Age + Gender + Education + Ethnicity + Migration + Region + Outsourcing \]
where
- Income is a continuous numeric variable indicating a the respondent’s income (weekly or annual, in different models)
- Age is a continuous numeric variable indicating the respondent’s age
- Gender is a categorical variable with three levels:
- Male (reference category)
- Female
- Other
- Education is a categorical variable indicating whether the respondent has a degree, with three levels:
- Yes (reference category)
- No
- Don’t know
- Ethnicity is a categorical variable with eight levels:
- White British (reference category)
- Arab/British Arab
- Asian/Asian British
- Black/African/Caribbean/Black British
- Mixed/Multiple ethnic group
- Other ethnic group
- Prefer not to say
- White other
- Don’t think of myself as any of these
- Migration is a categorical variable indicating when the respondent arrived in the UK, with 10 levels:
- I was born in the UK (reference category)
- Within the last year
- Within the last 3 years
- Within the last 5 years
- Within the last 10 years
- Within the last 15 years
- Within the last 20 years
- Within the last 30 years
- More than 30 years ago
- Prefer not to say
- Region is a categorical variable indicating the respodent’s region of residence, with 12 levels:
- London (reference category)
- East Midlands
- East of England
- North East
- North West
- Northern Ireland
- Scotland
- South East
- South West
- Wales
- West Midlands
- Yorkshire and the Humber
- Outsourcing is a categorical variable indicating whether the respondent is outsourced, with two levels:
- Not outsourced (reference category)
- Outsourced
The annual income model was statistically significant (R2 = 0.18, F(35, 8071) = 51.29, p < .001). The table below shows the coefficients for the annual income model.
| Annual income | |||
| Predictors | Estimates | CI | p |
| Intercept | 39068.13 | 37794.38 – 40341.88 | <0.001 |
| Age | 14.39 | -6.41 – 35.19 | 0.175 |
| Gender: Female | -7002.82 | -7535.16 – -6470.48 | <0.001 |
| Gender: Other | -6032.87 | -12748.83 – 683.09 | 0.078 |
| Gender: Prefer not to say | -2828.76 | -9792.72 – 4135.20 | 0.426 |
| Education: Don't have degree | -8170.64 | -8723.33 – -7617.95 | <0.001 |
| Education: Don't know | -9849.13 | -12104.71 – -7593.55 | <0.001 |
| Ethnicity: Arab/British Arab | -177.61 | -4873.46 – 4518.23 | 0.941 |
| Ethnicity: Asian/Asian British | -471.78 | -1573.79 – 630.22 | 0.401 |
| Ethnicity: Black/African/Caribbean/Black British | -1203.90 | -2816.77 – 408.97 | 0.143 |
| Ethnicity: Don't think of myself as any of these | -3198.11 | -12756.84 – 6360.62 | 0.512 |
| Ethnicity: Mixed/Multiple ethnic group | -1507.68 | -3488.56 – 473.20 | 0.136 |
| Ethnicity: Other ethnic group | 3596.90 | -998.30 – 8192.10 | 0.125 |
| Ethnicity: Prefer not to say | -82.72 | -5289.34 – 5123.89 | 0.975 |
| Ethnicity: White other | -637.07 | -2018.88 – 744.74 | 0.366 |
| Region: East Midlands | -5854.69 | -7085.16 – -4624.23 | <0.001 |
| Region: East of England | -4103.34 | -5262.01 – -2944.67 | <0.001 |
| Region: North East | -4834.89 | -6372.61 – -3297.16 | <0.001 |
| Region: North West | -4472.28 | -5597.32 – -3347.24 | <0.001 |
| Region: Northern Ireland | -6336.40 | -8132.24 – -4540.55 | <0.001 |
| Region: Scotland | -5448.95 | -6649.58 – -4248.32 | <0.001 |
| Region: South East | -3460.88 | -4512.27 – -2409.49 | <0.001 |
| Region: South West | -5748.69 | -6947.04 – -4550.34 | <0.001 |
| Region: Wales | -5215.03 | -6681.40 – -3748.66 | <0.001 |
| Region: West Midlands | -4759.33 | -5932.19 – -3586.48 | <0.001 |
| Region: Yorkshire and the Humber | -5451.06 | -6649.03 – -4253.09 | <0.001 |
| Outsourcing: Outsourced | -2995.19 | -3715.49 – -2274.89 | <0.001 |
| Migration: Arrived within the last year | -6032.95 | -8309.80 – -3756.10 | <0.001 |
| Migration: Arrived within the last 3 years | -2375.85 | -4406.64 – -345.06 | 0.022 |
| Migration: Arrived within the last 5 years | -1830.71 | -4132.93 – 471.51 | 0.119 |
| Migration: Arrived within the last 10 years | -691.71 | -2485.65 – 1102.24 | 0.450 |
| Migration: Arrived within the last 15 years | 747.68 | -1267.29 – 2762.65 | 0.467 |
| Migration: Arrived within the last 20 years | 1625.36 | -508.65 – 3759.37 | 0.135 |
| Migration: Arrived within the last 30 years | 2911.95 | 401.12 – 5422.79 | 0.023 |
| Migration: Arrived more than 30 years ago | -46.10 | -2002.94 – 1910.74 | 0.963 |
| Migration: Prefer not to say | -1667.72 | -5284.42 – 1948.98 | 0.366 |
| Observations | 8107 | ||
| R2 / R2 adjusted | 0.182 / 0.178 | ||
As expected, the model statistics for weekly income model were identical to the those of the annual income model. The model was statistically significant (R2 = 0.18, F(35, 8071) = 51.29, p < .001). The table below shows the coefficients for the weekly income model.
| Weekly income | |||
| Predictors | Estimates | CI | p |
| Intercept | 839.40 | 812.03 – 866.77 | <0.001 |
| Age | 0.31 | -0.14 – 0.76 | 0.175 |
| Gender: Female | -150.46 | -161.90 – -139.02 | <0.001 |
| Gender: Other | -129.62 | -273.92 – 14.68 | 0.078 |
| Gender: Prefer not to say | -60.78 | -210.40 – 88.85 | 0.426 |
| Education: Don't have degree | -175.55 | -187.43 – -163.68 | <0.001 |
| Education: Don't know | -211.61 | -260.08 – -163.15 | <0.001 |
| Ethnicity: Arab/British Arab | -3.82 | -104.71 – 97.08 | 0.941 |
| Ethnicity: Asian/Asian British | -10.14 | -33.81 – 13.54 | 0.401 |
| Ethnicity: Black/African/Caribbean/Black British | -25.87 | -60.52 – 8.79 | 0.143 |
| Ethnicity: Don't think of myself as any of these | -68.71 | -274.09 – 136.66 | 0.512 |
| Ethnicity: Mixed/Multiple ethnic group | -32.39 | -74.95 – 10.17 | 0.136 |
| Ethnicity: Other ethnic group | 77.28 | -21.45 – 176.01 | 0.125 |
| Ethnicity: Prefer not to say | -1.78 | -113.64 – 110.09 | 0.975 |
| Ethnicity: White other | -13.69 | -43.38 – 16.00 | 0.366 |
| Region: East Midlands | -125.79 | -152.23 – -99.35 | <0.001 |
| Region: East of England | -88.16 | -113.06 – -63.27 | <0.001 |
| Region: North East | -103.88 | -136.92 – -70.84 | <0.001 |
| Region: North West | -96.09 | -120.26 – -71.92 | <0.001 |
| Region: Northern Ireland | -136.14 | -174.73 – -97.56 | <0.001 |
| Region: Scotland | -117.07 | -142.87 – -91.28 | <0.001 |
| Region: South East | -74.36 | -96.95 – -51.77 | <0.001 |
| Region: South West | -123.51 | -149.26 – -97.77 | <0.001 |
| Region: Wales | -112.05 | -143.55 – -80.54 | <0.001 |
| Region: West Midlands | -102.26 | -127.46 – -77.06 | <0.001 |
| Region: Yorkshire and the Humber | -117.12 | -142.86 – -91.38 | <0.001 |
| Outsourcing: Outsourced | -64.35 | -79.83 – -48.88 | <0.001 |
| Migration: Arrived within the last year | -129.62 | -178.54 – -80.70 | <0.001 |
| Migration: Arrived within the last 3 years | -51.05 | -94.68 – -7.41 | 0.022 |
| Migration: Arrived within the last 5 years | -39.33 | -88.80 – 10.13 | 0.119 |
| Migration: Arrived within the last 10 years | -14.86 | -53.41 – 23.68 | 0.450 |
| Migration: Arrived within the last 15 years | 16.06 | -27.23 – 59.36 | 0.467 |
| Migration: Arrived within the last 20 years | 34.92 | -10.93 – 80.77 | 0.135 |
| Migration: Arrived within the last 30 years | 62.56 | 8.62 – 116.51 | 0.023 |
| Migration: Arrived more than 30 years ago | -0.99 | -43.03 – 41.05 | 0.963 |
| Migration: Prefer not to say | -35.83 | -113.54 – 41.87 | 0.366 |
| Observations | 8107 | ||
| R2 / R2 adjusted | 0.182 / 0.178 | ||
3.4.2 Gender pay gap
The above model was also used to assess a possible gender pay gap. As shown in the preceding two tables, there is a significant difference in pay between men and women. Annually, women earn £7002.82 less than men. Per week, women earn £150.46 less than men.
We next explored whether outsourcing compounds this gender pay gap by adding an interaction term into the previous models so that
\[ Income = Age + Gender + Education + Ethnicity + Migration + Region + Outsourcing + Gender:Outsourcing \]
For both models, adding the interaction effect did not improve model fit (R2 = 0.18, F(3, 8068) = 0.74, p = 0.531). The tables below show the coefficients for each model.
| Annual income | |||
| Predictors | Estimates | CI | p |
| Intercept | 39092.47 | 37809.50 – 40375.44 | <0.001 |
| Age | 14.30 | -6.51 – 35.10 | 0.178 |
| Gender: Female | -7004.18 | -7586.05 – -6422.30 | <0.001 |
| Gender: Other | -3445.67 | -10995.26 – 4103.92 | 0.371 |
| Gender: Prefer not to say | -2634.20 | -9886.89 – 4618.49 | 0.477 |
| Education: Has degree | -8169.16 | -8722.09 – -7616.22 | <0.001 |
| Education: Don't know | -9849.18 | -12104.91 – -7593.45 | <0.001 |
| Ethnicity: Arab/British Arab | -170.96 | -4867.74 – 4525.81 | 0.943 |
| Ethnicity: Asian/Asian British | -472.69 | -1574.78 – 629.39 | 0.401 |
| Ethnicity: Black/African/Caribbean/Black British | -1203.91 | -2816.96 – 409.13 | 0.143 |
| Ethnicity: Don't think of myself as any of these | -3193.67 | -12753.23 – 6365.88 | 0.513 |
| Ethnicity: Mixed/Multiple ethnic group | -1511.45 | -3492.47 – 469.58 | 0.135 |
| Ethnicity: Other ethnic group | 3593.79 | -1002.26 – 8189.84 | 0.125 |
| Ethnicity: Prefer not to say | -81.55 | -5288.77 – 5125.67 | 0.976 |
| Ethnicity: White other | -601.41 | -1984.70 – 781.89 | 0.394 |
| Region: East Midlands | -5879.63 | -7110.82 – -4648.44 | <0.001 |
| Region: East of England | -4135.62 | -5295.46 – -2975.79 | <0.001 |
| Region: North East | -4865.76 | -6404.23 – -3327.29 | <0.001 |
| Region: North West | -4502.26 | -5628.38 – -3376.14 | <0.001 |
| Region: Northern Ireland | -6358.52 | -8158.35 – -4558.69 | <0.001 |
| Region: Scotland | -5476.68 | -6678.10 – -4275.26 | <0.001 |
| Region: South East | -3488.28 | -4540.37 – -2436.18 | <0.001 |
| Region: South West | -5772.53 | -6971.45 – -4573.60 | <0.001 |
| Region: Wales | -5238.94 | -6705.78 – -3772.10 | <0.001 |
| Region: West Midlands | -4783.07 | -5956.82 – -3609.31 | <0.001 |
| Region: Yorkshire and the Humber | -5477.80 | -6676.53 – -4279.07 | <0.001 |
| Outsourcing: Outsourced | -2979.46 | -3945.17 – -2013.76 | <0.001 |
| Migration: Arrived within the last year | -6043.23 | -8320.40 – -3766.06 | <0.001 |
| Migration: Arrived within the last 3 years | -2386.09 | -4418.13 – -354.05 | 0.021 |
| Migration: Arrived within the last 5 years | -1849.05 | -4152.16 – 454.06 | 0.116 |
| Migration: Arrived within the last 10 years | -719.66 | -2514.13 – 1074.81 | 0.432 |
| Migration: Arrived within the last 15 years | 718.31 | -1297.19 – 2733.81 | 0.485 |
| Migration: Arrived within the last 20 years | 1602.21 | -532.38 – 3736.80 | 0.141 |
| Migration: Arrived within the last 30 years | 2893.95 | 382.76 – 5405.14 | 0.024 |
| Migration: Arrived more than 30 years ago | -58.23 | -2016.55 – 1900.09 | 0.954 |
| Migration: Prefer not to say | -1683.14 | -5300.38 – 1934.09 | 0.362 |
| Interaction: Outsourcing x Gender Female | 18.18 | -1412.01 – 1448.37 | 0.980 |
| Interaction: Outsourcing x Gender Other | -12395.16 | -28915.23 – 4124.91 | 0.141 |
| Interaction: Outsourcing x Gender Prefer not to say | -2506.28 | -28505.03 – 23492.47 | 0.850 |
| Observations | 8107 | ||
| R2 / R2 adjusted | 0.182 / 0.178 | ||
| Weekly income | |||
| Predictors | Estimates | CI | p |
| Intercept | 839.92 | 812.36 – 867.49 | <0.001 |
| Age | 0.31 | -0.14 – 0.75 | 0.178 |
| Gender: Female | -150.49 | -162.99 – -137.99 | <0.001 |
| Gender: Other | -74.03 | -236.24 – 88.18 | 0.371 |
| Gender: Prefer not to say | -56.60 | -212.43 – 99.23 | 0.477 |
| Education: Has degree | -175.52 | -187.40 – -163.64 | <0.001 |
| Education: Don't know | -211.62 | -260.08 – -163.15 | <0.001 |
| Ethnicity: Arab/British Arab | -3.67 | -104.59 – 97.24 | 0.943 |
| Ethnicity: Asian/Asian British | -10.16 | -33.84 – 13.52 | 0.401 |
| Ethnicity: Black/African/Caribbean/Black British | -25.87 | -60.52 – 8.79 | 0.143 |
| Ethnicity: Don't think of myself as any of these | -68.62 | -274.01 – 136.77 | 0.513 |
| Ethnicity: Mixed/Multiple ethnic group | -32.47 | -75.04 – 10.09 | 0.135 |
| Ethnicity: Other ethnic group | 77.21 | -21.53 – 175.96 | 0.125 |
| Ethnicity: Prefer not to say | -1.75 | -113.63 – 110.13 | 0.976 |
| Ethnicity: White other | -12.92 | -42.64 – 16.80 | 0.394 |
| Region: East Midlands | -126.33 | -152.78 – -99.87 | <0.001 |
| Region: East of England | -88.86 | -113.78 – -63.94 | <0.001 |
| Region: North East | -104.54 | -137.60 – -71.49 | <0.001 |
| Region: North West | -96.73 | -120.93 – -72.54 | <0.001 |
| Region: Northern Ireland | -136.62 | -175.29 – -97.95 | <0.001 |
| Region: Scotland | -117.67 | -143.48 – -91.86 | <0.001 |
| Region: South East | -74.95 | -97.55 – -52.34 | <0.001 |
| Region: South West | -124.03 | -149.79 – -98.27 | <0.001 |
| Region: Wales | -112.56 | -144.08 – -81.05 | <0.001 |
| Region: West Midlands | -102.77 | -127.99 – -77.55 | <0.001 |
| Region: Yorkshire and the Humber | -117.69 | -143.45 – -91.94 | <0.001 |
| Outsourcing: Outsourced | -64.02 | -84.76 – -43.27 | <0.001 |
| Migration: Arrived within the last year | -129.84 | -178.77 – -80.92 | <0.001 |
| Migration: Arrived within the last 3 years | -51.27 | -94.93 – -7.61 | 0.021 |
| Migration: Arrived within the last 5 years | -39.73 | -89.21 – 9.76 | 0.116 |
| Migration: Arrived within the last 10 years | -15.46 | -54.02 – 23.09 | 0.432 |
| Migration: Arrived within the last 15 years | 15.43 | -27.87 – 58.74 | 0.485 |
| Migration: Arrived within the last 20 years | 34.42 | -11.44 – 80.29 | 0.141 |
| Migration: Arrived within the last 30 years | 62.18 | 8.22 – 116.13 | 0.024 |
| Migration: Arrived more than 30 years ago | -1.25 | -43.33 – 40.82 | 0.954 |
| Migration: Prefer not to say | -36.16 | -113.88 – 41.56 | 0.362 |
| Interaction: Outsourcing x Gender Female | 0.39 | -30.34 – 31.12 | 0.980 |
| Interaction: Outsourcing x Gender Other | -266.32 | -621.26 – 88.63 | 0.141 |
| Interaction: Outsourcing x Gender Prefer not to say | -53.85 | -612.45 – 504.75 | 0.850 |
| Observations | 8107 | ||
| R2 / R2 adjusted | 0.182 / 0.178 | ||
The interaction term is non-significant. Estimated marginal means show that:
- Among not outsourced workers, men are paid £7004.18 more than women
- Among outsourced workers, men are paid £6986 more than women
- Among men, not outsourced workers are paid £2979.46 more than outsourced workers.
- Among women, not outsourced workers are paid £2961.28 more than outsourced workers.
The plot below illustrates the main effects that men are paid more than women and that outsourced men and women are paid less than non-outsourced men and women. The lack of interaction indicates that the difference in pay between men and women does not significantly differ between outsourced and non-outsourced people.
3.4.3 Demographic models
3.4.3.1 Ethnicity
Several regressions were run to assess the likelihood of being outsourced from demographics. These models underlie the claims in the report in relation to ethnicity, migration, and gender.
The overall model was defined as:
\[ Outsourcing = Ethnicity + Age + Gender + Education + Region + Migration \]
where
- Outsourcing is a categorical variable indicating whether the respondent is outsourced, with two levels:
- Not outsourced (reference category)
- Outsourced
- Age is a continuous numeric variable indicating the respondent’s age
- Gender is a categorical variable with three levels:
- Male (reference category)
- Female
- Other
- Education is a categorical variable indicating whether the respondent has a degree, with three levels:
- Yes (reference category)
- No
- Don’t know
- Migration is a categorical variable indicating when the respondent arrived in the UK, with 10 levels:
- I was born in the UK (reference category)
- Within the last year
- Within the last 3 years
- Within the last 5 years
- Within the last 10 years
- Within the last 15 years
- Within the last 20 years
- Within the last 30 years
- More than 30 years ago
- Prefer not to say
- Region is a categorical variable indicating the respodent’s region of residence, with 12 levels:
- London (reference category)
- East Midlands
- East of England
- North East
- North West
- Northern Ireland
- Scotland
- South East
- South West
- Wales
- West Midlands
- Yorkshire and the Humber
For this exploration we modelled ethnicity in three ways.
- As a categorical variable with four levels:
- White (reference category)
- Not White
- Don’t think of myself as any of these
- Prefer not say
- As a categorical variable with eight levels:
- White British (reference category)
- Arab/British Arab
- Asian/Asian British
- Black/African/Caribbean/Black British
- Don’t think of myself as any of these
- Mixed/Multiple ethnic group
- Other ethnic group
- Prefer not to say
- White other
- As a categorical variable with 21 levels:
- English/Welsh/Scottish/Northern Irish/British (reference category)
- Irish
- Gypsy or Irish Traveller
- Roma
- Any other White background
- White and Black Caribbean
- White and Black African
- White and Asian
- Any other Mixed/Multiple ethnic background
- Indian
- Pakistani
- Bangladeshi
- Chinese
- Any other Asian background
- African
- Caribbean
- Any other Black, Black British, or Caribbean background
- Arab
- Any other ethnic group
- Don’t think of myself as any of these
- Prefer not to say
We used svyglm() from the survey package to construct survey-weighted generalised linear models. This approach allows us to take into account survey weights to produce design-based standard errors by assuming a ‘quasibinomial’ distribution to the data. Specifically, the survey-weighted data contains overdispersion; the variance is greater than expected by a binomial distribution (which assumes variance = mean(1 - mean)). The quasibinomial distribution estimates a dispersion parameter that allows the variance to be greater than expected by the true binomial distribution. For more information see Lumley, Thomas, and Alastair Scott. ‘Fitting Regression Models to Survey Data’. Statistical Science 32, no. 2 (2017): 265–780.
We used Rao–Scott adjusted Wald tests to compare nested survey-weighted models fit using a quasibinomial family. This method accounts for the survey design and is appropriate given that quasi-likelihood models do not support likelihood-ratio testing. For more information see Rao, J. N. K., and A. J. Scott. ‘On Chi-Squared Tests for Multiway Contingency Tables with Cell Proportions Estimated from Survey Data’. The Annals of Statistics 12, no. 1 (March 1984): 46–60. https://doi.org/10.1214/aos/1176346391.
For model 1, a saturated model including all variables was a significantly better fit to the data than an intercept-only model, F(29, 9782) = 8.72, p < .001. The table below shows the model coefficients.
| Outsourcing | |||
| Predictors | Odds Ratios | CI | p |
| Intercept | 0.65 | 0.49 – 0.86 | 0.002 |
| Ethnicity: Not White | 1.38 | 1.15 – 1.66 | 0.001 |
| Ethnicity: Don't think of myself as any of these | 2.50 | 0.76 – 8.18 | 0.131 |
| Ethnicity: Prefer not to say | 1.41 | 0.39 – 5.14 | 0.600 |
| Age | 0.98 | 0.97 – 0.98 | <0.001 |
| Gender: Female | 0.69 | 0.61 – 0.79 | <0.001 |
| Gender: Other | 0.87 | 0.22 – 3.50 | 0.842 |
| Gender: Prefer not to say | 0.82 | 0.26 – 2.53 | 0.724 |
| Education: Don't have degree | 1.06 | 0.93 – 1.21 | 0.372 |
| Education: Don't know | 1.14 | 0.64 – 2.04 | 0.650 |
| Region: East Midlands | 0.94 | 0.71 – 1.25 | 0.676 |
| Region: East of England | 0.59 | 0.44 – 0.79 | 0.001 |
| Region: North East | 0.63 | 0.44 – 0.91 | 0.012 |
| Region: North West | 0.85 | 0.66 – 1.08 | 0.185 |
| Region: Northern Ireland | 0.70 | 0.46 – 1.07 | 0.098 |
| Region: Scotland | 0.68 | 0.50 – 0.93 | 0.015 |
| Region: South East | 0.62 | 0.49 – 0.79 | <0.001 |
| Region: South West | 0.68 | 0.52 – 0.90 | 0.006 |
| Region: Wales | 0.91 | 0.66 – 1.25 | 0.551 |
| Region: West Midlands | 0.83 | 0.65 – 1.08 | 0.166 |
| Region: Yorkshire and the Humber | 0.68 | 0.52 – 0.89 | 0.005 |
| Migration: Arrived within the last year | 1.69 | 1.13 – 2.52 | 0.010 |
| Migration: Arrived within the last 3 years | 1.03 | 0.68 – 1.56 | 0.885 |
| Migration: Arrived within the last 5 years | 1.22 | 0.78 – 1.89 | 0.386 |
| Migration: Arrived within the last 10 years | 1.61 | 1.14 – 2.26 | 0.006 |
| Migration: Arrived within the last 15 years | 1.58 | 1.05 – 2.36 | 0.026 |
| Migration: Arrived within the last 20 years | 1.54 | 1.00 – 2.35 | 0.048 |
| Migration: Arrived within the last 30 years | 0.45 | 0.22 – 0.92 | 0.029 |
| Migration: Arrived more than 30 years ago | 2.01 | 1.31 – 3.08 | 0.001 |
| Migration: Prefer not to say | 1.22 | 0.71 – 2.10 | 0.472 |
| Observations | 9812 | ||
For model 2, a saturated model including all variables was a significantly better fit to the data than an intercept-only model, F(34, 9777) = 8.07, p < .001. The table below shows the model coefficients.
| Outsourcing | |||
| Predictors | Odds Ratios | CI | p |
| Intercept | 0.67 | 0.50 – 0.88 | 0.004 |
| Ethnicity: Arab/British Arab | 1.98 | 0.77 – 5.11 | 0.159 |
| Ethnicity: Asian/Asian British | 1.24 | 0.96 – 1.59 | 0.094 |
| Ethnicity: Black/African/Caribbean/Black British | 1.47 | 1.10 – 1.96 | 0.010 |
| Ethnicity: Don't think of myself as any of these | 2.35 | 0.72 – 7.73 | 0.159 |
| Ethnicity: Mixed/Multiple ethnic group | 1.37 | 1.00 – 1.89 | 0.053 |
| Ethnicity: Other ethnic group | 1.03 | 0.27 – 4.01 | 0.963 |
| Ethnicity: Prefer not to say | 1.37 | 0.37 – 5.01 | 0.639 |
| Ethnicity: White other | 0.81 | 0.58 – 1.11 | 0.191 |
| Age | 0.98 | 0.97 – 0.98 | <0.001 |
| Gender: Female | 0.69 | 0.61 – 0.78 | <0.001 |
| Gender: Other | 0.88 | 0.21 – 3.62 | 0.860 |
| Gender: Prefer not to say | 0.81 | 0.26 – 2.50 | 0.711 |
| Education: Don't have degree | 1.06 | 0.93 – 1.21 | 0.357 |
| Education: Don't know | 1.16 | 0.65 – 2.06 | 0.618 |
| Region: East Midlands | 0.93 | 0.71 – 1.23 | 0.628 |
| Region: East of England | 0.58 | 0.43 – 0.78 | <0.001 |
| Region: North East | 0.62 | 0.43 – 0.89 | 0.009 |
| Region: North West | 0.83 | 0.65 – 1.07 | 0.143 |
| Region: Northern Ireland | 0.72 | 0.47 – 1.10 | 0.129 |
| Region: Scotland | 0.67 | 0.50 – 0.91 | 0.011 |
| Region: South East | 0.61 | 0.48 – 0.78 | <0.001 |
| Region: South West | 0.67 | 0.50 – 0.88 | 0.004 |
| Region: Wales | 0.89 | 0.65 – 1.22 | 0.476 |
| Region: West Midlands | 0.83 | 0.64 – 1.07 | 0.147 |
| Region: Yorkshire and the Humber | 0.67 | 0.51 – 0.88 | 0.004 |
| Migration: Arrived within the last year | 1.71 | 1.13 – 2.60 | 0.011 |
| Migration: Arrived within the last 3 years | 1.08 | 0.71 – 1.65 | 0.713 |
| Migration: Arrived within the last 5 years | 1.31 | 0.83 – 2.07 | 0.249 |
| Migration: Arrived within the last 10 years | 1.80 | 1.23 – 2.63 | 0.002 |
| Migration: Arrived within the last 15 years | 1.74 | 1.13 – 2.68 | 0.013 |
| Migration: Arrived within the last 20 years | 1.68 | 1.07 – 2.63 | 0.024 |
| Migration: Arrived within the last 30 years | 0.49 | 0.23 – 1.02 | 0.056 |
| Migration: Arrived more than 30 years ago | 2.09 | 1.36 – 3.22 | 0.001 |
| Migration: Prefer not to say | 1.24 | 0.72 – 2.14 | 0.430 |
| Observations | 9812 | ||
For model 3, a saturated model including all variables was a significantly better fit to the data than an intercept-only model, F(46, 9765) = 6.88, p < .001. The table below shows the model coefficients.
| Outsourcing | |||
| Predictors | Odds Ratios | CI | p |
| Intercept | 0.67 | 0.50 – 0.88 | 0.004 |
| Ethnicity: Irish | 0.76 | 0.40 – 1.45 | 0.410 |
| Ethnicity: Gypsy or Irish Traveller | 1.00 | 0.18 – 5.50 | 0.999 |
| Ethnicity: Roma | 1.25 | 0.32 – 4.87 | 0.751 |
| Ethnicity: Any other White background | 0.80 | 0.55 – 1.15 | 0.225 |
| Ethnicity: White and Black Caribbean | 0.49 | 0.25 – 0.95 | 0.035 |
| Ethnicity: White and Black African | 2.66 | 1.51 – 4.66 | 0.001 |
| Ethnicity: White and Asian | 1.19 | 0.58 – 2.46 | 0.634 |
| Ethnicity: Any other Mixed/Multiple ethnic background | 2.00 | 1.08 – 3.67 | 0.026 |
| Ethnicity: Indian | 1.18 | 0.81 – 1.71 | 0.396 |
| Ethnicity: Pakistani | 2.17 | 1.45 – 3.25 | <0.001 |
| Ethnicity: Bangladeshi | 1.32 | 0.71 – 2.45 | 0.375 |
| Ethnicity: Chinese | 0.62 | 0.33 – 1.19 | 0.150 |
| Ethnicity: Any other Asian background | 1.19 | 0.70 – 2.04 | 0.521 |
| Ethnicity: African | 1.44 | 1.04 – 2.00 | 0.027 |
| Ethnicity: Caribbean | 1.27 | 0.63 – 2.54 | 0.499 |
| Ethnicity: Any other Black, Black British, or Caribbean background | 1.84 | 0.88 – 3.86 | 0.106 |
| Ethnicity: Arab | 1.97 | 0.76 – 5.10 | 0.164 |
| Ethnicity: Any other ethnic group | 1.03 | 0.26 – 4.03 | 0.963 |
| Ethnicity: Don't think of myself as any of these | 2.31 | 0.71 – 7.57 | 0.166 |
| Ethnicity: Prefer not to say | 1.37 | 0.37 – 5.04 | 0.632 |
| Age | 0.98 | 0.97 – 0.98 | <0.001 |
| Gender: Female | 0.69 | 0.61 – 0.79 | <0.001 |
| Gender: Other | 0.88 | 0.22 – 3.63 | 0.865 |
| Gender: Prefer not to say | 0.83 | 0.27 – 2.56 | 0.747 |
| Education: Don't have degree | 1.06 | 0.93 – 1.20 | 0.402 |
| Education: Don't know | 1.16 | 0.65 – 2.06 | 0.623 |
| Region: East Midlands | 0.92 | 0.70 – 1.22 | 0.583 |
| Region: East of England | 0.57 | 0.42 – 0.77 | <0.001 |
| Region: North East | 0.61 | 0.43 – 0.88 | 0.008 |
| Region: North West | 0.81 | 0.63 – 1.05 | 0.111 |
| Region: Northern Ireland | 0.73 | 0.46 – 1.14 | 0.165 |
| Region: Scotland | 0.67 | 0.49 – 0.91 | 0.009 |
| Region: South East | 0.60 | 0.48 – 0.77 | <0.001 |
| Region: South West | 0.66 | 0.50 – 0.87 | 0.003 |
| Region: Wales | 0.88 | 0.64 – 1.21 | 0.423 |
| Region: West Midlands | 0.81 | 0.62 – 1.05 | 0.104 |
| Region: Yorkshire and the Humber | 0.65 | 0.50 – 0.86 | 0.002 |
| Migration: Arrived within the last year | 1.76 | 1.14 – 2.72 | 0.011 |
| Migration: Arrived within the last 3 years | 1.10 | 0.72 – 1.69 | 0.652 |
| Migration: Arrived within the last 5 years | 1.23 | 0.77 – 1.97 | 0.389 |
| Migration: Arrived within the last 10 years | 1.76 | 1.20 – 2.57 | 0.004 |
| Migration: Arrived within the last 15 years | 1.84 | 1.18 – 2.87 | 0.007 |
| Migration: Arrived within the last 20 years | 1.69 | 1.07 – 2.66 | 0.024 |
| Migration: Arrived within the last 30 years | 0.49 | 0.24 – 1.01 | 0.054 |
| Migration: Arrived more than 30 years ago | 2.11 | 1.37 – 3.26 | 0.001 |
| Migration: Prefer not to say | 1.22 | 0.70 – 2.12 | 0.478 |
| Observations | 9812 | ||
3.4.3.2 Migration
We next focus on predicting whether a person was outsourced based on wehther the person was born in the UK. This binary variable was constructed by collapsing the 10-level migration variable down into two levels, so that “I was born in the UK” becomes “Born in UK”, and all levels apart from “I was born in the UK” and “Prefer not to say” become “Not born in UK”.
A saturated model including all variables was a significantly better fit to the data than an intercept-only model, F(39, 9772) = 7.52, p < .001. The table below shows the model coefficients.
| Outsourcing | |||
| Predictors | Odds Ratios | CI | p |
| Intercept | 0.66 | 0.50 – 0.88 | 0.004 |
| Migration: Not born in the UK | 1.52 | 1.22 – 1.89 | <0.001 |
| Migration: Prefer not to say | 1.23 | 0.71 – 2.13 | 0.465 |
| Ethnicity: Irish | 0.76 | 0.40 – 1.44 | 0.396 |
| Ethnicity: Gypsy or Irish Traveller | 1.04 | 0.20 – 5.55 | 0.962 |
| Ethnicity: Roma | 1.11 | 0.26 – 4.73 | 0.883 |
| Ethnicity: Any other White background | 0.82 | 0.57 – 1.16 | 0.253 |
| Ethnicity: White and Black Caribbean | 0.48 | 0.25 – 0.94 | 0.033 |
| Ethnicity: White and Black African | 2.56 | 1.45 – 4.54 | 0.001 |
| Ethnicity: White and Asian | 1.22 | 0.58 – 2.57 | 0.595 |
| Ethnicity: Any other Mixed/Multiple ethnic background | 1.81 | 1.04 – 3.16 | 0.037 |
| Ethnicity: Indian | 1.13 | 0.77 – 1.65 | 0.530 |
| Ethnicity: Pakistani | 2.13 | 1.40 – 3.22 | <0.001 |
| Ethnicity: Bangladeshi | 1.27 | 0.68 – 2.37 | 0.457 |
| Ethnicity: Chinese | 0.60 | 0.31 – 1.15 | 0.125 |
| Ethnicity: Any other Asian background | 1.21 | 0.72 – 2.04 | 0.480 |
| Ethnicity: African | 1.46 | 1.08 – 1.98 | 0.014 |
| Ethnicity: Caribbean | 1.24 | 0.61 – 2.51 | 0.545 |
| Ethnicity: Any other Black, Black British, or Caribbean background | 1.73 | 0.83 – 3.64 | 0.146 |
| Ethnicity: Arab | 2.04 | 0.80 – 5.22 | 0.136 |
| Ethnicity: Any other ethnic group | 1.04 | 0.27 – 4.01 | 0.951 |
| Ethnicity: Don't think of myself as any of these | 2.28 | 0.71 – 7.27 | 0.164 |
| Ethnicity: Prefer not to say | 1.29 | 0.35 – 4.77 | 0.704 |
| Age | 0.98 | 0.97 – 0.98 | <0.001 |
| Gender: Female | 0.70 | 0.61 – 0.79 | <0.001 |
| Gender: Other | 0.88 | 0.22 – 3.62 | 0.865 |
| Gender: Prefer not to say | 0.82 | 0.27 – 2.54 | 0.737 |
| Education: Don't have degree | 1.05 | 0.93 – 1.20 | 0.420 |
| Education: Don't know | 1.19 | 0.67 – 2.11 | 0.561 |
| Region: East Midlands | 0.93 | 0.70 – 1.23 | 0.598 |
| Region: East of England | 0.56 | 0.42 – 0.76 | <0.001 |
| Region: North East | 0.61 | 0.42 – 0.88 | 0.007 |
| Region: North West | 0.81 | 0.63 – 1.04 | 0.100 |
| Region: Northern Ireland | 0.72 | 0.46 – 1.13 | 0.157 |
| Region: Scotland | 0.66 | 0.49 – 0.90 | 0.008 |
| Region: South East | 0.60 | 0.47 – 0.76 | <0.001 |
| Region: South West | 0.66 | 0.50 – 0.87 | 0.003 |
| Region: Wales | 0.87 | 0.64 – 1.20 | 0.403 |
| Region: West Midlands | 0.80 | 0.62 – 1.04 | 0.098 |
| Region: Yorkshire and the Humber | 0.65 | 0.49 – 0.85 | 0.002 |
| Observations | 9812 | ||
3.4.3.3 Gender
We used the same generalised linear model as in the previous section to estimate the effect of Gender on outsourcing, where Gender is a categorical variable with four levels:
- Male (reference category)
- Female
- Prefer not to say
- Other
The model indicates that women are 0.7 times as likely (i.e. 30% less likely) to be outsourced than men.
3.4.3.4 Age
Again using the same model, we found that age was a significant predictor of the likelihood of being outsourced. The model indicates that for each year older a worker is, they are 0.98 times as likely (i.e. 2% less likely) to be outsourced.
We also explored how age predicted whether a person was on low pay. The model formula is:
\[ Income Group = Age + Outsourcing + Ethnicity + Gender + Education + Region + Migration \]
A saturated model including all variables was a significantly better fit to the data than an intercept-only model, X^2(80) = 22488.5220035, p < .001. The table below shows the model coefficients.
| income_group | estimate | std.error | statistic | p.value | conf.low | conf.high | sig |
|---|---|---|---|---|---|---|---|
| Mid | |||||||
| (Intercept) | 0.134 | 0.171 | -11.740 | 0.000 | 0.096 | 0.187 | *** |
| Age | 1.007 | 0.003 | 2.637 | 0.008 | 1.002 | 1.012 | ** |
| outsourcing_statusOutsourced | 1.362 | 0.088 | 3.504 | 0.000 | 1.146 | 1.619 | *** |
| Ethnicity_collapsed_disaggregatedIrish | 1.347 | 0.297 | 1.003 | 0.316 | 0.753 | 2.412 | |
| Ethnicity_collapsed_disaggregatedGypsy or Irish Traveller | 0.253 | 1.063 | -1.295 | 0.195 | 0.031 | 2.028 | |
| Ethnicity_collapsed_disaggregatedRoma | 0.000 | 0.211 | -70.286 | 0.000 | 0.000 | 0.000 | *** |
| Ethnicity_collapsed_disaggregatedAny other White background | 1.032 | 0.195 | 0.163 | 0.870 | 0.704 | 1.514 | |
| Ethnicity_collapsed_disaggregatedWhite and Black Caribbean | 1.227 | 0.337 | 0.609 | 0.543 | 0.635 | 2.374 | |
| Ethnicity_collapsed_disaggregatedWhite and Black African | 0.971 | 0.460 | -0.064 | 0.949 | 0.394 | 2.392 | |
| Ethnicity_collapsed_disaggregatedWhite and Asian | 1.770 | 0.324 | 1.765 | 0.077 | 0.939 | 3.337 | |
| Ethnicity_collapsed_disaggregatedAny other Mixed / Multiple ethnic background | 1.906 | 0.332 | 1.940 | 0.052 | 0.993 | 3.657 | |
| Ethnicity_collapsed_disaggregatedIndian | 0.841 | 0.228 | -0.761 | 0.446 | 0.538 | 1.314 | |
| Ethnicity_collapsed_disaggregatedPakistani | 1.313 | 0.249 | 1.092 | 0.275 | 0.805 | 2.141 | |
| Ethnicity_collapsed_disaggregatedBangladeshi | 1.612 | 0.378 | 1.262 | 0.207 | 0.768 | 3.385 | |
| Ethnicity_collapsed_disaggregatedChinese | 0.718 | 0.362 | -0.916 | 0.360 | 0.353 | 1.459 | |
| Ethnicity_collapsed_disaggregatedAny other Asian background | 1.266 | 0.360 | 0.656 | 0.512 | 0.625 | 2.564 | |
| Ethnicity_collapsed_disaggregatedAfrican | 1.246 | 0.198 | 1.110 | 0.267 | 0.845 | 1.838 | |
| Ethnicity_collapsed_disaggregatedCaribbean | 0.938 | 0.396 | -0.163 | 0.870 | 0.432 | 2.036 | |
| Ethnicity_collapsed_disaggregatedAny other Black, Black British, or Caribbean background | 1.195 | 0.475 | 0.376 | 0.707 | 0.471 | 3.030 | |
| Ethnicity_collapsed_disaggregatedArab | 1.715 | 0.661 | 0.816 | 0.415 | 0.469 | 6.263 | |
| Ethnicity_collapsed_disaggregatedAny other ethnic group | 0.629 | 1.158 | -0.401 | 0.689 | 0.065 | 6.081 | |
| Ethnicity_collapsed_disaggregatedDon’t think of myself as any of these | 0.350 | 0.897 | -1.171 | 0.241 | 0.060 | 2.028 | |
| Ethnicity_collapsed_disaggregatedPrefer not to say | 1.676 | 0.647 | 0.799 | 0.424 | 0.472 | 5.951 | |
| GenderFemale | 2.754 | 0.071 | 14.266 | 0.000 | 2.396 | 3.165 | *** |
| GenderOther | 3.946 | 0.663 | 2.071 | 0.038 | 1.076 | 14.468 | * |
| GenderPrefer not to say | 2.024 | 0.841 | 0.838 | 0.402 | 0.389 | 10.525 | |
| Has_DegreeNo | 1.912 | 0.067 | 9.684 | 0.000 | 1.677 | 2.180 | *** |
| Has_DegreeDon't know | 3.073 | 0.304 | 3.693 | 0.000 | 1.693 | 5.576 | *** |
| RegionEast Midlands | 1.106 | 0.153 | 0.662 | 0.508 | 0.820 | 1.492 | |
| RegionEast of England | 1.124 | 0.158 | 0.740 | 0.459 | 0.825 | 1.531 | |
| RegionNorth East | 0.838 | 0.192 | -0.920 | 0.358 | 0.575 | 1.221 | |
| RegionNorth West | 0.760 | 0.146 | -1.881 | 0.060 | 0.571 | 1.012 | |
| RegionNorthern Ireland | 1.122 | 0.213 | 0.540 | 0.589 | 0.739 | 1.702 | |
| RegionScotland | 1.133 | 0.158 | 0.788 | 0.431 | 0.831 | 1.544 | |
| RegionSouth East | 0.982 | 0.133 | -0.134 | 0.894 | 0.757 | 1.274 | |
| RegionSouth West | 0.911 | 0.148 | -0.630 | 0.528 | 0.681 | 1.218 | |
| RegionWales | 0.663 | 0.194 | -2.120 | 0.034 | 0.453 | 0.969 | * |
| RegionWest Midlands | 0.935 | 0.145 | -0.464 | 0.643 | 0.703 | 1.243 | |
| RegionYorkshire and the Humber | 0.927 | 0.149 | -0.508 | 0.612 | 0.692 | 1.242 | |
| BORNUK_binaryNot born in UK | 0.900 | 0.133 | -0.789 | 0.430 | 0.694 | 1.168 | |
| BORNUK_binaryPrefer not to say | 1.889 | 0.429 | 1.482 | 0.138 | 0.815 | 4.381 | |
| High | |||||||
| (Intercept) | 0.506 | 0.156 | -4.378 | 0.000 | 0.373 | 0.686 | *** |
| Age | 1.011 | 0.002 | 4.480 | 0.000 | 1.006 | 1.015 | *** |
| outsourcing_statusOutsourced | 0.673 | 0.092 | -4.325 | 0.000 | 0.562 | 0.805 | *** |
| Ethnicity_collapsed_disaggregatedIrish | 0.609 | 0.313 | -1.585 | 0.113 | 0.330 | 1.125 | |
| Ethnicity_collapsed_disaggregatedGypsy or Irish Traveller | 0.948 | 1.276 | -0.041 | 0.967 | 0.078 | 11.573 | |
| Ethnicity_collapsed_disaggregatedRoma | 0.000 | 0.199 | -74.594 | 0.000 | 0.000 | 0.000 | *** |
| Ethnicity_collapsed_disaggregatedAny other White background | 1.144 | 0.178 | 0.755 | 0.451 | 0.807 | 1.622 | |
| Ethnicity_collapsed_disaggregatedWhite and Black Caribbean | 0.858 | 0.383 | -0.399 | 0.690 | 0.405 | 1.819 | |
| Ethnicity_collapsed_disaggregatedWhite and Black African | 0.766 | 0.378 | -0.704 | 0.481 | 0.366 | 1.607 | |
| Ethnicity_collapsed_disaggregatedWhite and Asian | 1.346 | 0.331 | 0.897 | 0.370 | 0.703 | 2.575 | |
| Ethnicity_collapsed_disaggregatedAny other Mixed / Multiple ethnic background | 0.939 | 0.340 | -0.185 | 0.853 | 0.482 | 1.828 | |
| Ethnicity_collapsed_disaggregatedIndian | 1.130 | 0.200 | 0.613 | 0.540 | 0.764 | 1.672 | |
| Ethnicity_collapsed_disaggregatedPakistani | 0.478 | 0.293 | -2.518 | 0.012 | 0.269 | 0.849 | * |
| Ethnicity_collapsed_disaggregatedBangladeshi | 0.881 | 0.412 | -0.307 | 0.759 | 0.393 | 1.977 | |
| Ethnicity_collapsed_disaggregatedChinese | 1.143 | 0.305 | 0.439 | 0.661 | 0.629 | 2.078 | |
| Ethnicity_collapsed_disaggregatedAny other Asian background | 0.665 | 0.359 | -1.138 | 0.255 | 0.329 | 1.343 | |
| Ethnicity_collapsed_disaggregatedAfrican | 0.659 | 0.198 | -2.106 | 0.035 | 0.447 | 0.972 | * |
| Ethnicity_collapsed_disaggregatedCaribbean | 1.279 | 0.292 | 0.843 | 0.399 | 0.722 | 2.266 | |
| Ethnicity_collapsed_disaggregatedAny other Black, Black British, or Caribbean background | 0.753 | 0.425 | -0.668 | 0.504 | 0.327 | 1.731 | |
| Ethnicity_collapsed_disaggregatedArab | 2.159 | 0.587 | 1.312 | 0.189 | 0.684 | 6.817 | |
| Ethnicity_collapsed_disaggregatedAny other ethnic group | 1.929 | 0.541 | 1.216 | 0.224 | 0.669 | 5.567 | |
| Ethnicity_collapsed_disaggregatedDon’t think of myself as any of these | 0.000 | 0.194 | -74.405 | 0.000 | 0.000 | 0.000 | *** |
| Ethnicity_collapsed_disaggregatedPrefer not to say | 1.025 | 0.632 | 0.038 | 0.969 | 0.297 | 3.538 | |
| GenderFemale | 0.494 | 0.065 | -10.886 | 0.000 | 0.435 | 0.561 | *** |
| GenderOther | 1.112 | 0.876 | 0.122 | 0.903 | 0.200 | 6.192 | |
| GenderPrefer not to say | 0.968 | 0.714 | -0.046 | 0.963 | 0.239 | 3.919 | |
| Has_DegreeNo | 0.300 | 0.070 | -17.209 | 0.000 | 0.262 | 0.344 | *** |
| Has_DegreeDon't know | 0.494 | 0.354 | -1.988 | 0.047 | 0.247 | 0.990 | * |
| RegionEast Midlands | 1.606 | 0.157 | 3.018 | 0.003 | 1.181 | 2.185 | ** |
| RegionEast of England | 1.837 | 0.160 | 3.798 | 0.000 | 1.342 | 2.515 | *** |
| RegionNorth East | 1.547 | 0.190 | 2.297 | 0.022 | 1.066 | 2.244 | * |
| RegionNorth West | 1.509 | 0.144 | 2.858 | 0.004 | 1.138 | 2.000 | ** |
| RegionNorthern Ireland | 1.713 | 0.241 | 2.233 | 0.026 | 1.068 | 2.749 | * |
| RegionScotland | 0.995 | 0.162 | -0.028 | 0.978 | 0.725 | 1.366 | |
| RegionSouth East | 1.415 | 0.140 | 2.487 | 0.013 | 1.076 | 1.861 | * |
| RegionSouth West | 1.226 | 0.156 | 1.305 | 0.192 | 0.903 | 1.665 | |
| RegionWales | 1.228 | 0.178 | 1.150 | 0.250 | 0.866 | 1.741 | |
| RegionWest Midlands | 1.455 | 0.154 | 2.431 | 0.015 | 1.075 | 1.970 | * |
| RegionYorkshire and the Humber | 1.333 | 0.160 | 1.791 | 0.073 | 0.973 | 1.826 | |
| BORNUK_binaryNot born in UK | 0.766 | 0.129 | -2.063 | 0.039 | 0.595 | 0.987 | * |
| BORNUK_binaryPrefer not to say | 1.428 | 0.528 | 0.675 | 0.500 | 0.507 | 4.023 | |
3.4.3.5 Ethnicity-migration interaction
We next explored whether there was an interaction between ethnicity and migration in predicting outsourcing using generalised linear models by adding an interaction effect to the model predicting outsourcing above so that the model formula is:
\[ Outsourcing = Ethnicity + Age + Gender + Educaton + Region + Migration + Ethnicity:Migration \]
where Ethnicity:Migration represents the interaction term.
We did this twice: first for the aggregated eight-level ethnicity variable, and then for the disaggregated 21-level variable.
3.4.3.5.1 Ethnicity 9
A model including the ethnicity:migration interaction term had significantly improved fit compared to a model without the interaction term, F(13, 9771) = 33.88, p < .001. The table below shows the model coefficients.
| Outsourcing | |||
| Predictors | Odds Ratios | CI | p |
| Intercept | 0.62 | 0.47 – 0.82 | 0.001 |
| Migration: Not born in the UK | 2.16 | 1.60 – 2.91 | <0.001 |
| Migration: Prefer not to say | 1.88 | 0.94 – 3.79 | 0.076 |
| Ethnicity: Arab/British Arab | 1.86 | 0.39 – 8.76 | 0.435 |
| Ethnicity: Asian/Asian British | 1.48 | 1.09 – 2.00 | 0.011 |
| Ethnicity: Black/African/Caribbean/Black British | 1.53 | 1.00 – 2.35 | 0.049 |
| Ethnicity: Don't think of myself as any of these | 5.49 | 1.00 – 30.12 | 0.050 |
| Ethnicity: Mixed/Multiple ethnic group | 1.16 | 0.81 – 1.67 | 0.406 |
| Ethnicity: Other ethnic group | 3.35 | 0.65 – 17.23 | 0.147 |
| Ethnicity: Prefer not to say | 1.99 | 0.44 – 8.97 | 0.369 |
| Ethnicity: White other | 1.06 | 0.64 – 1.74 | 0.825 |
| Age | 0.98 | 0.97 – 0.98 | <0.001 |
| Gender: Female | 0.69 | 0.61 – 0.79 | <0.001 |
| Gender: Other | 0.88 | 0.22 – 3.52 | 0.857 |
| Gender: Prefer not to say | 0.75 | 0.23 – 2.45 | 0.636 |
| Education: Don't have degree | 1.06 | 0.94 – 1.21 | 0.341 |
| Education: Don't know | 1.24 | 0.70 – 2.20 | 0.467 |
| Region: East Midlands | 0.95 | 0.72 – 1.26 | 0.744 |
| Region: East of England | 0.59 | 0.44 – 0.80 | 0.001 |
| Region: North East | 0.64 | 0.44 – 0.91 | 0.015 |
| Region: North West | 0.85 | 0.66 – 1.10 | 0.209 |
| Region: Northern Ireland | 0.69 | 0.44 – 1.08 | 0.106 |
| Region: Scotland | 0.69 | 0.51 – 0.94 | 0.020 |
| Region: South East | 0.62 | 0.49 – 0.79 | <0.001 |
| Region: South West | 0.69 | 0.52 – 0.91 | 0.009 |
| Region: Wales | 0.92 | 0.67 – 1.26 | 0.597 |
| Region: West Midlands | 0.83 | 0.64 – 1.08 | 0.159 |
| Region: Yorkshire and the Humber | 0.68 | 0.52 – 0.90 | 0.007 |
| Interaction: Not born in UK x Arab/Arab British | 0.86 | 0.12 – 6.12 | 0.881 |
| Interaction: Not born in UK x Asian/Asian British | 0.52 | 0.32 – 0.87 | 0.012 |
| Interaction: Prefer not to say x Asian/Asian British | 0.26 | 0.05 – 1.27 | 0.096 |
| Interaction: Not born in UK x Black/African/Caribbean/Black British | 0.66 | 0.37 – 1.18 | 0.161 |
| Interaction: Prefer not to say x Black/African/Caribbean/Black British | 0.97 | 0.23 – 3.99 | 0.964 |
| Interaction: Not born in UK x Don't think of myself as any of these | 0.13 | 0.01 – 1.80 | 0.128 |
| Interaction: Not born in UK x Mixed/Multiple ethnic group | 1.27 | 0.60 – 2.70 | 0.530 |
| Interaction: Prefer not to say x Mixed/Multiple ethnic group | 0.39 | 0.03 – 4.64 | 0.453 |
| Interaction: Not born in UK x Other ethnic group | 0.00 | 0.00 – 0.00 | <0.001 |
| Interaction: Not born in UK x Prefer not to say | 0.49 | 0.04 – 5.99 | 0.577 |
| Interaction: Prefer not to say x Prefer not to say | 0.00 | 0.00 – 0.00 | <0.001 |
| Interaction: Not born in UK x White other | 0.53 | 0.28 – 1.01 | 0.053 |
| Interaction: Prefer not to say x White other | 0.59 | 0.10 – 3.61 | 0.564 |
| Observations | 9812 | ||
3.4.3.5.1.1 Post-hoc
We explored the interaction effect using targeted contrasts comparing
- The effect of each ethnicity versus “White British” within each level of migration.
- The effect of “Not born in UK” versus “Born in UK” within each level of ethnicity
Contrasts were calculated using survey::svycontrast() and p values were adjusted for multiple comparisons using the FDR method (Benjamini and Hochberg, 1995). Results were only considered where the sample for the contrast was greater than 10.
Exploring the effect of each ethnicity versus “White British” within each level of migration, we found that, among people not born in the UK, White other workers were 0.56 times as likely (i.e., 44% less likely) to be outsourced than “White British” people.
No differences by ethnicity were observed among people born in the UK.
Examining the effect of “Not born in UK” versus “Born in UK” within each ethnicity, we found
- among “White British”, workers not born in the UK are 2.16 times more likely to be outsourced than workers born in the UK.
- among people of Mixed/multiple ethnic groups, workers not born in UK are 2.74 times more likely to be outsourced than workers born in the UK.
No significant differences between people born and not born in the UK were observed for any other ethnicities. The figure below shows these effects.
3.4.3.5.2 Ethnicity 21
A model including the ethnicity:migration interaction term had significantly improved fit compared to a model without the interaction term, F(32, 9740) = 64.66, p < .001. The table below shows the model coefficients.
| Outsourcing | |||
| Predictors | Odds Ratios | CI | p |
| Intercept | 0.63 | 0.47 – 0.83 | 0.001 |
| Migration: Not born in the UK | 2.15 | 1.59 – 2.90 | <0.001 |
| Migration: Prefer not to say | 1.89 | 0.94 – 3.79 | 0.075 |
| Ethnicity: Irish | 0.93 | 0.44 – 1.96 | 0.846 |
| Ethnicity: Gypsy or Irish Traveller | 1.67 | 0.31 – 8.85 | 0.547 |
| Ethnicity: Roma | 0.81 | 0.19 – 3.49 | 0.775 |
| Ethnicity: Any other White background | 1.11 | 0.55 – 2.26 | 0.769 |
| Ethnicity: White and Black Caribbean | 0.53 | 0.27 – 1.04 | 0.066 |
| Ethnicity: White and Black African | 3.38 | 1.68 – 6.82 | 0.001 |
| Ethnicity: White and Asian | 0.90 | 0.37 – 2.19 | 0.817 |
| Ethnicity: Any other Mixed/Multiple ethnic background | 1.87 | 0.96 – 3.65 | 0.067 |
| Ethnicity: Indian | 1.32 | 0.80 – 2.19 | 0.280 |
| Ethnicity: Pakistani | 2.67 | 1.68 – 4.25 | <0.001 |
| Ethnicity: Bangladeshi | 1.81 | 0.84 – 3.86 | 0.128 |
| Ethnicity: Chinese | 0.53 | 0.16 – 1.74 | 0.299 |
| Ethnicity: Any other Asian background | 1.06 | 0.36 – 3.11 | 0.916 |
| Ethnicity: African | 1.52 | 0.87 – 2.64 | 0.140 |
| Ethnicity: Caribbean | 1.12 | 0.49 – 2.53 | 0.787 |
| Ethnicity: Any other Black, Black British, or Caribbean background | 2.60 | 1.05 – 6.41 | 0.038 |
| Ethnicity: Arab | 1.85 | 0.39 – 8.69 | 0.435 |
| Ethnicity: Any other ethnic group | 3.34 | 0.65 – 17.32 | 0.150 |
| Ethnicity: Don't think of myself as any of these | 5.43 | 1.00 – 29.50 | 0.050 |
| Ethnicity: Prefer not to say | 1.99 | 0.44 – 8.97 | 0.370 |
| Age | 0.98 | 0.97 – 0.98 | <0.001 |
| Gender: Female | 0.69 | 0.61 – 0.79 | <0.001 |
| Gender: Other | 0.87 | 0.22 – 3.44 | 0.846 |
| Gender: Prefer not to say | 0.77 | 0.24 – 2.50 | 0.662 |
| Education: Don't have degree | 1.06 | 0.93 – 1.20 | 0.405 |
| Education: Don't know | 1.21 | 0.67 – 2.18 | 0.519 |
| Region: East Midlands | 0.94 | 0.71 – 1.25 | 0.692 |
| Region: East of England | 0.59 | 0.44 – 0.80 | 0.001 |
| Region: North East | 0.63 | 0.44 – 0.91 | 0.014 |
| Region: North West | 0.84 | 0.65 – 1.08 | 0.179 |
| Region: Northern Ireland | 0.71 | 0.44 – 1.14 | 0.153 |
| Region: Scotland | 0.69 | 0.50 – 0.93 | 0.017 |
| Region: South East | 0.62 | 0.49 – 0.79 | <0.001 |
| Region: South West | 0.68 | 0.51 – 0.90 | 0.007 |
| Region: Wales | 0.90 | 0.65 – 1.24 | 0.524 |
| Region: West Midlands | 0.80 | 0.61 – 1.04 | 0.093 |
| Region: Yorkshire and the Humber | 0.67 | 0.51 – 0.88 | 0.004 |
| Interaction: Not born in UK x Irish | 0.28 | 0.07 – 1.19 | 0.085 |
| Interaction: Prefer not to say x Irish | 1.31 | 0.09 – 18.48 | 0.842 |
| Interaction: Not born in UK x Gypsy or Irish Traveller | 0.00 | 0.00 – 0.00 | <0.001 |
| Interaction: Not born in UK x Any other White background | 0.52 | 0.23 – 1.18 | 0.118 |
| Interaction: Prefer not to say x Any other White background | 0.38 | 0.04 – 3.92 | 0.413 |
| Interaction: Not born in UK x White and Black Caribbean | 0.00 | 0.00 – 0.00 | <0.001 |
| Interaction: Prefer not to say x White and Black Caribbean | 0.00 | 0.00 – 0.00 | <0.001 |
| Interaction: Not born in UK x White and Black African | 0.46 | 0.14 – 1.52 | 0.203 |
| Interaction: Prefer not to say x White and Black African | 0.00 | 0.00 – 0.00 | <0.001 |
| Interaction: Not born in UK x White and Asian | 2.76 | 0.48 – 15.70 | 0.253 |
| Interaction: Not born in UK x Any other Mixed/Multiple ethnic background | 0.69 | 0.21 – 2.26 | 0.541 |
| Interaction: Prefer not to say x Any other Mixed/Multiple ethnic background | 1.10 | 0.03 – 41.99 | 0.960 |
| Interaction: Not born in UK x Indian | 0.55 | 0.26 – 1.17 | 0.120 |
| Interaction: Prefer not to say x Indian | 0.38 | 0.04 – 3.31 | 0.380 |
| Interaction: Not born in UK x Pakistani | 0.43 | 0.17 – 1.09 | 0.074 |
| Interaction: Prefer not to say x Pakistani | 0.00 | 0.00 – 0.00 | <0.001 |
| Interaction: Not born in UK x Bangladeshi | 0.38 | 0.10 – 1.48 | 0.162 |
| Interaction: Prefer not to say x Bangladeshi | 0.30 | 0.03 – 2.99 | 0.305 |
| Interaction: Not born in UK x Chinese | 0.88 | 0.21 – 3.70 | 0.864 |
| Interaction: Not born in UK x Any other Asian background | 0.90 | 0.26 – 3.13 | 0.864 |
| Interaction: Prefer not to say x Any other Asian background | 0.00 | 0.00 – 0.00 | <0.001 |
| Interaction: Not born in UK x African | 0.69 | 0.35 – 1.36 | 0.278 |
| Interaction: Prefer not to say x African | 0.89 | 0.19 – 4.18 | 0.886 |
| Interaction: Not born in UK x Caribbean | 0.84 | 0.14 – 5.19 | 0.851 |
| Interaction: Prefer not to say x Caribbean | 8523821.18 | 897250.55 – 80975740.17 | <0.001 |
| Interaction: Not born in UK x Any other Black, Black British, or Caribbean background | 0.25 | 0.05 – 1.15 | 0.075 |
| Interaction: Prefer not to say x Any other Black, Black British, or Caribbean background | 0.00 | 0.00 – 0.00 | <0.001 |
| Interaction: Not born in UK x Arab | 0.86 | 0.12 – 6.11 | 0.880 |
| Interaction: Not born in UK x Any other ethnic group | 0.00 | 0.00 – 0.00 | <0.001 |
| Interaction: Not born in UK x Don't think of myself as any of these | 0.13 | 0.01 – 1.82 | 0.129 |
| Interaction: Not born in UK x Prefer not to say | 0.49 | 0.04 – 5.98 | 0.579 |
| Interaction: Prefer not to say x Prefer not to say | 0.00 | 0.00 – 0.00 | <0.001 |
| Observations | 9812 | ||
3.4.3.5.2.1 Post-hoc
We explored the interaction effect using targeted contrasts comparing
- The effect of each ethnicity versus “English / Welsh / Scottish / Northern Irish / British” within each level of migration.
- The effect of “Not born in UK” versus “Born in UK” within each level of ethnicity
Contrasts were calculated using survey::svycontrast() and p values were adjusted for multiple comparisons using the FDR method (Benjamini and Hochberg, 1995) and results were only considered where the sample for the contrast was greater than 10.
Exploring the effect of each ethnicity versus “English / Welsh / Scottish / Northern Irish / British” within each level of migration, we found that, among people born in the UK
- White and Black African people were 3.38 times more likely to be outsourced than “English / Welsh / Scottish / Northern Irish / British” people.
- Pakistani people were 2.67 times more likely to be outsourced than “English / Welsh / Scottish / Northern Irish / British” people.
Among people not born in the UK, no significant differences between ethnicities were observed. The figure below shows the effects for “English / Welsh / Scottish / Northern Irish / British”, “White and Black African”, and Pakistani respondents.
Examining the effect of “Not born in UK” versus “Born in UK” within each level of ethnicity, we found that among “English / Welsh / Scottish / Northern Irish / British”, workers not born in the UK are 2.15 times more likely to be outsourced than workers born in the UK.
No significant differences between people born and not born in the UK were observed for any other ethnicities. The figure below shows these effects.
3.4.3.6 Ethnicity-outsourced interaction
A generalised linear model was constructed to test whether the interaction between ethnicity and outsourcing predicted whether a person had a low income.
\[ Income Group = Outsourcing + Ethnicity + Age + Gender + Education + Region + Migration + Outsourcing:Ethnicity \]
As in preceding sections, we constructed three models; one for the binary ethnicity variable, one for the eight-level ethnicity variable, and one for the 21-level ethnicity variable.
3.4.3.6.1 Ethnicity binary
A saturated model including the interaction term was not a significantly better fit to the data than an intercept-only model, X^2(2) = 0.9400127, p = 0.625. The table below shows the model coefficients of the simpler model without the interaction term.
| income_group | estimate | std.error | statistic | p.value | conf.low | conf.high | sig |
|---|---|---|---|---|---|---|---|
| Low | |||||||
| (Intercept) | 0.136 | 0.171 | -11.689 | 0.000 | 0.097 | 0.190 | *** |
| outsourcing_statusOutsourced | 1.358 | 0.088 | 3.463 | 0.001 | 1.142 | 1.614 | *** |
| Ethnicity_binaryNon-White | 1.075 | 0.114 | 0.633 | 0.526 | 0.859 | 1.345 | |
| Age | 1.007 | 0.003 | 2.652 | 0.008 | 1.002 | 1.013 | ** |
| GenderFemale | 2.755 | 0.071 | 14.280 | 0.000 | 2.397 | 3.166 | *** |
| GenderOther | 3.948 | 0.664 | 2.068 | 0.039 | 1.074 | 14.508 | * |
| GenderPrefer not to say | 2.062 | 0.844 | 0.858 | 0.391 | 0.394 | 10.786 | |
| Has_DegreeNo | 1.920 | 0.067 | 9.779 | 0.000 | 1.685 | 2.189 | *** |
| Has_DegreeDon't know | 3.229 | 0.309 | 3.796 | 0.000 | 1.763 | 5.916 | *** |
| RegionEast Midlands | 1.077 | 0.152 | 0.487 | 0.627 | 0.799 | 1.452 | |
| RegionEast of England | 1.104 | 0.158 | 0.625 | 0.532 | 0.810 | 1.503 | |
| RegionNorth East | 0.822 | 0.191 | -1.027 | 0.304 | 0.565 | 1.195 | |
| RegionNorth West | 0.737 | 0.145 | -2.108 | 0.035 | 0.555 | 0.979 | * |
| RegionNorthern Ireland | 1.157 | 0.207 | 0.704 | 0.481 | 0.771 | 1.738 | |
| RegionScotland | 1.095 | 0.157 | 0.580 | 0.562 | 0.805 | 1.490 | |
| RegionSouth East | 0.973 | 0.132 | -0.207 | 0.836 | 0.751 | 1.261 | |
| RegionSouth West | 0.885 | 0.147 | -0.827 | 0.408 | 0.663 | 1.182 | |
| RegionWales | 0.650 | 0.192 | -2.240 | 0.025 | 0.446 | 0.948 | * |
| RegionWest Midlands | 0.906 | 0.144 | -0.687 | 0.492 | 0.683 | 1.202 | |
| RegionYorkshire and the Humber | 0.907 | 0.149 | -0.659 | 0.510 | 0.678 | 1.213 | |
| BORNUK_labelledWithin the last year | 1.374 | 0.247 | 1.286 | 0.199 | 0.846 | 2.230 | |
| BORNUK_labelledWithin the last 3 years | 0.901 | 0.244 | -0.425 | 0.671 | 0.558 | 1.455 | |
| BORNUK_labelledWithin the last 5 years | 1.000 | 0.267 | -0.001 | 0.999 | 0.593 | 1.686 | |
| BORNUK_labelledWithin the last 10 years | 0.925 | 0.221 | -0.353 | 0.724 | 0.600 | 1.425 | |
| BORNUK_labelledWithin the last 15 years | 0.911 | 0.254 | -0.367 | 0.714 | 0.554 | 1.498 | |
| BORNUK_labelledWithin the last 20 years | 0.834 | 0.244 | -0.743 | 0.458 | 0.517 | 1.346 | |
| BORNUK_labelledWithin the last 30 years | 0.622 | 0.425 | -1.116 | 0.264 | 0.270 | 1.432 | |
| BORNUK_labelledMore than 30 years ago | 0.894 | 0.264 | -0.425 | 0.671 | 0.533 | 1.500 | |
| BORNUK_labelledPrefer not to say | 2.226 | 0.429 | 1.864 | 0.062 | 0.960 | 5.163 | |
| High | |||||||
| (Intercept) | 0.516 | 0.156 | -4.239 | 0.000 | 0.380 | 0.700 | *** |
| outsourcing_statusOutsourced | 0.661 | 0.092 | -4.504 | 0.000 | 0.552 | 0.792 | *** |
| Ethnicity_binaryNon-White | 0.985 | 0.108 | -0.140 | 0.889 | 0.797 | 1.218 | |
| Age | 1.010 | 0.002 | 4.257 | 0.000 | 1.005 | 1.015 | *** |
| GenderFemale | 0.497 | 0.065 | -10.814 | 0.000 | 0.438 | 0.564 | *** |
| GenderOther | 1.120 | 0.878 | 0.129 | 0.897 | 0.200 | 6.263 | |
| GenderPrefer not to say | 0.935 | 0.696 | -0.096 | 0.923 | 0.239 | 3.662 | |
| Has_DegreeNo | 0.301 | 0.070 | -17.176 | 0.000 | 0.262 | 0.345 | *** |
| Has_DegreeDon't know | 0.499 | 0.348 | -2.000 | 0.046 | 0.252 | 0.986 | * |
| RegionEast Midlands | 1.596 | 0.156 | 2.986 | 0.003 | 1.174 | 2.168 | ** |
| RegionEast of England | 1.839 | 0.159 | 3.832 | 0.000 | 1.347 | 2.511 | *** |
| RegionNorth East | 1.542 | 0.188 | 2.307 | 0.021 | 1.067 | 2.227 | * |
| RegionNorth West | 1.484 | 0.143 | 2.761 | 0.006 | 1.121 | 1.965 | ** |
| RegionNorthern Ireland | 1.561 | 0.231 | 1.931 | 0.054 | 0.993 | 2.453 | |
| RegionScotland | 0.994 | 0.162 | -0.040 | 0.968 | 0.724 | 1.364 | |
| RegionSouth East | 1.411 | 0.139 | 2.469 | 0.014 | 1.074 | 1.854 | * |
| RegionSouth West | 1.224 | 0.156 | 1.298 | 0.194 | 0.902 | 1.660 | |
| RegionWales | 1.226 | 0.178 | 1.149 | 0.251 | 0.866 | 1.738 | |
| RegionWest Midlands | 1.430 | 0.154 | 2.322 | 0.020 | 1.057 | 1.935 | * |
| RegionYorkshire and the Humber | 1.306 | 0.159 | 1.683 | 0.092 | 0.957 | 1.783 | |
| BORNUK_labelledWithin the last year | 0.323 | 0.337 | -3.358 | 0.001 | 0.167 | 0.624 | *** |
| BORNUK_labelledWithin the last 3 years | 0.488 | 0.270 | -2.659 | 0.008 | 0.288 | 0.828 | ** |
| BORNUK_labelledWithin the last 5 years | 0.760 | 0.243 | -1.127 | 0.260 | 0.472 | 1.224 | |
| BORNUK_labelledWithin the last 10 years | 0.754 | 0.200 | -1.414 | 0.157 | 0.510 | 1.115 | |
| BORNUK_labelledWithin the last 15 years | 1.011 | 0.233 | 0.045 | 0.964 | 0.640 | 1.595 | |
| BORNUK_labelledWithin the last 20 years | 0.999 | 0.241 | -0.006 | 0.996 | 0.622 | 1.602 | |
| BORNUK_labelledWithin the last 30 years | 0.701 | 0.320 | -1.111 | 0.266 | 0.374 | 1.312 | |
| BORNUK_labelledMore than 30 years ago | 1.078 | 0.221 | 0.341 | 0.733 | 0.699 | 1.665 | |
| BORNUK_labelledPrefer not to say | 1.412 | 0.539 | 0.640 | 0.522 | 0.491 | 4.062 | |
3.4.3.6.2 Ethnicity 9
A model including the ethnicity:outsourcing interaction term significantly improved model fit compared to a model without the interaction term, X^2(12) = 238.2925359, p < .001. The table below shows the model coefficients.
| income_group | estimate | std.error | statistic | p.value | conf.low | conf.high | sig |
|---|---|---|---|---|---|---|---|
| Mid | |||||||
| (Intercept) | 0.132 | 0.172 | -11.768 | 0.000 | 0.094 | 0.184 | *** |
| outsourcing_statusOutsourced | 1.428 | 0.102 | 3.480 | 0.001 | 1.168 | 1.746 | *** |
| Ethnicity_collapsedArab/British Arab | 1.844 | 0.827 | 0.740 | 0.459 | 0.365 | 9.326 | |
| Ethnicity_collapsedAsian/Asian British | 1.076 | 0.179 | 0.410 | 0.682 | 0.758 | 1.527 | |
| Ethnicity_collapsedBlack/African/Caribbean/Black British | 1.349 | 0.196 | 1.526 | 0.127 | 0.918 | 1.982 | |
| Ethnicity_collapsedMixed/Multiple ethnic group | 1.066 | 0.213 | 0.299 | 0.765 | 0.702 | 1.618 | |
| Ethnicity_collapsedOther ethnic group | 0.000 | 0.271 | -51.997 | 0.000 | 0.000 | 0.000 | *** |
| Ethnicity_collapsedWhite other | 1.128 | 0.193 | 0.626 | 0.531 | 0.773 | 1.647 | |
| Age | 1.007 | 0.003 | 2.762 | 0.006 | 1.002 | 1.013 | ** |
| GenderFemale | 2.789 | 0.071 | 14.431 | 0.000 | 2.426 | 3.205 | *** |
| GenderOther | 4.090 | 0.671 | 2.100 | 0.036 | 1.098 | 15.232 | * |
| GenderPrefer not to say | 2.078 | 0.845 | 0.866 | 0.387 | 0.397 | 10.880 | |
| Has_DegreeNo | 1.923 | 0.067 | 9.813 | 0.000 | 1.688 | 2.192 | *** |
| Has_DegreeDon't know | 3.215 | 0.311 | 3.754 | 0.000 | 1.747 | 5.915 | *** |
| RegionEast Midlands | 1.076 | 0.153 | 0.479 | 0.632 | 0.798 | 1.452 | |
| RegionEast of England | 1.098 | 0.158 | 0.592 | 0.554 | 0.805 | 1.497 | |
| RegionNorth East | 0.827 | 0.192 | -0.992 | 0.321 | 0.567 | 1.204 | |
| RegionNorth West | 0.731 | 0.145 | -2.156 | 0.031 | 0.550 | 0.972 | * |
| RegionNorthern Ireland | 1.131 | 0.206 | 0.595 | 0.552 | 0.754 | 1.695 | |
| RegionScotland | 1.105 | 0.158 | 0.630 | 0.528 | 0.811 | 1.506 | |
| RegionSouth East | 0.969 | 0.133 | -0.233 | 0.816 | 0.746 | 1.259 | |
| RegionSouth West | 0.880 | 0.148 | -0.868 | 0.385 | 0.659 | 1.175 | |
| RegionWales | 0.649 | 0.192 | -2.244 | 0.025 | 0.445 | 0.947 | * |
| RegionWest Midlands | 0.901 | 0.145 | -0.722 | 0.470 | 0.678 | 1.196 | |
| RegionYorkshire and the Humber | 0.913 | 0.149 | -0.611 | 0.541 | 0.682 | 1.223 | |
| BORNUK_labelledWithin the last year | 1.461 | 0.266 | 1.426 | 0.154 | 0.868 | 2.459 | |
| BORNUK_labelledWithin the last 3 years | 0.898 | 0.245 | -0.439 | 0.660 | 0.555 | 1.452 | |
| BORNUK_labelledWithin the last 5 years | 0.953 | 0.278 | -0.174 | 0.862 | 0.553 | 1.642 | |
| BORNUK_labelledWithin the last 10 years | 0.918 | 0.251 | -0.341 | 0.733 | 0.561 | 1.502 | |
| BORNUK_labelledWithin the last 15 years | 0.895 | 0.274 | -0.404 | 0.686 | 0.523 | 1.531 | |
| BORNUK_labelledWithin the last 20 years | 0.837 | 0.262 | -0.679 | 0.497 | 0.501 | 1.398 | |
| BORNUK_labelledWithin the last 30 years | 0.573 | 0.418 | -1.331 | 0.183 | 0.253 | 1.301 | |
| BORNUK_labelledMore than 30 years ago | 0.897 | 0.266 | -0.411 | 0.681 | 0.532 | 1.510 | |
| BORNUK_labelledPrefer not to say | 2.270 | 0.443 | 1.849 | 0.064 | 0.952 | 5.411 | |
| outsourcing_statusOutsourced:Ethnicity_collapsedArab/British Arab | 0.690 | 1.414 | -0.263 | 0.793 | 0.043 | 11.020 | |
| outsourcing_statusOutsourced:Ethnicity_collapsedAsian/Asian British | 0.824 | 0.297 | -0.654 | 0.513 | 0.461 | 1.473 | |
| outsourcing_statusOutsourced:Ethnicity_collapsedBlack/African/Caribbean/Black British | 0.380 | 0.367 | -2.637 | 0.008 | 0.185 | 0.780 | ** |
| outsourcing_statusOutsourced:Ethnicity_collapsedMixed/Multiple ethnic group | 2.858 | 0.418 | 2.515 | 0.012 | 1.261 | 6.480 | * |
| outsourcing_statusOutsourced:Ethnicity_collapsedOther ethnic group | 7934713.109 | 1.500 | 10.591 | 0.000 | 419527.941 | 150072655.492 | *** |
| outsourcing_statusOutsourced:Ethnicity_collapsedWhite other | 0.719 | 0.339 | -0.972 | 0.331 | 0.370 | 1.398 | |
| High | |||||||
| (Intercept) | 0.522 | 0.158 | -4.113 | 0.000 | 0.383 | 0.711 | *** |
| outsourcing_statusOutsourced | 0.638 | 0.109 | -4.134 | 0.000 | 0.515 | 0.789 | *** |
| Ethnicity_collapsedArab/British Arab | 2.984 | 0.773 | 1.414 | 0.157 | 0.656 | 13.581 | |
| Ethnicity_collapsedAsian/Asian British | 0.917 | 0.161 | -0.538 | 0.591 | 0.669 | 1.257 | |
| Ethnicity_collapsedBlack/African/Caribbean/Black British | 0.850 | 0.192 | -0.850 | 0.395 | 0.583 | 1.237 | |
| Ethnicity_collapsedMixed/Multiple ethnic group | 0.850 | 0.204 | -0.795 | 0.427 | 0.570 | 1.268 | |
| Ethnicity_collapsedOther ethnic group | 1.749 | 0.544 | 1.027 | 0.304 | 0.602 | 5.082 | |
| Ethnicity_collapsedWhite other | 0.931 | 0.163 | -0.435 | 0.663 | 0.676 | 1.283 | |
| Age | 1.010 | 0.002 | 4.267 | 0.000 | 1.005 | 1.015 | *** |
| GenderFemale | 0.497 | 0.065 | -10.800 | 0.000 | 0.437 | 0.564 | *** |
| GenderOther | 1.125 | 0.882 | 0.133 | 0.894 | 0.200 | 6.336 | |
| GenderPrefer not to say | 0.938 | 0.699 | -0.092 | 0.927 | 0.238 | 3.689 | |
| Has_DegreeNo | 0.301 | 0.070 | -17.210 | 0.000 | 0.262 | 0.345 | *** |
| Has_DegreeDon't know | 0.501 | 0.348 | -1.988 | 0.047 | 0.253 | 0.990 | * |
| RegionEast Midlands | 1.599 | 0.157 | 2.986 | 0.003 | 1.175 | 2.177 | ** |
| RegionEast of England | 1.836 | 0.159 | 3.811 | 0.000 | 1.343 | 2.509 | *** |
| RegionNorth East | 1.535 | 0.190 | 2.257 | 0.024 | 1.058 | 2.226 | * |
| RegionNorth West | 1.473 | 0.144 | 2.688 | 0.007 | 1.111 | 1.954 | ** |
| RegionNorthern Ireland | 1.580 | 0.233 | 1.962 | 0.050 | 1.000 | 2.494 | * |
| RegionScotland | 0.988 | 0.163 | -0.075 | 0.940 | 0.718 | 1.360 | |
| RegionSouth East | 1.394 | 0.140 | 2.373 | 0.018 | 1.060 | 1.834 | * |
| RegionSouth West | 1.207 | 0.157 | 1.204 | 0.229 | 0.888 | 1.641 | |
| RegionWales | 1.214 | 0.178 | 1.087 | 0.277 | 0.856 | 1.721 | |
| RegionWest Midlands | 1.428 | 0.155 | 2.301 | 0.021 | 1.054 | 1.934 | * |
| RegionYorkshire and the Humber | 1.296 | 0.161 | 1.616 | 0.106 | 0.946 | 1.776 | |
| BORNUK_labelledWithin the last year | 0.333 | 0.348 | -3.164 | 0.002 | 0.169 | 0.658 | ** |
| BORNUK_labelledWithin the last 3 years | 0.509 | 0.270 | -2.503 | 0.012 | 0.300 | 0.864 | * |
| BORNUK_labelledWithin the last 5 years | 0.757 | 0.254 | -1.098 | 0.272 | 0.460 | 1.245 | |
| BORNUK_labelledWithin the last 10 years | 0.774 | 0.218 | -1.176 | 0.240 | 0.505 | 1.186 | |
| BORNUK_labelledWithin the last 15 years | 1.034 | 0.250 | 0.132 | 0.895 | 0.634 | 1.686 | |
| BORNUK_labelledWithin the last 20 years | 1.052 | 0.250 | 0.204 | 0.838 | 0.645 | 1.717 | |
| BORNUK_labelledWithin the last 30 years | 0.719 | 0.329 | -1.001 | 0.317 | 0.377 | 1.371 | |
| BORNUK_labelledMore than 30 years ago | 1.080 | 0.222 | 0.346 | 0.729 | 0.699 | 1.668 | |
| BORNUK_labelledPrefer not to say | 1.450 | 0.542 | 0.685 | 0.493 | 0.501 | 4.192 | |
| outsourcing_statusOutsourced:Ethnicity_collapsedArab/British Arab | 0.340 | 1.419 | -0.760 | 0.447 | 0.021 | 5.488 | |
| outsourcing_statusOutsourced:Ethnicity_collapsedAsian/Asian British | 1.079 | 0.299 | 0.254 | 0.800 | 0.600 | 1.939 | |
| outsourcing_statusOutsourced:Ethnicity_collapsedBlack/African/Caribbean/Black British | 1.305 | 0.306 | 0.870 | 0.384 | 0.716 | 2.378 | |
| outsourcing_statusOutsourced:Ethnicity_collapsedMixed/Multiple ethnic group | 1.998 | 0.442 | 1.564 | 0.118 | 0.839 | 4.755 | |
| outsourcing_statusOutsourced:Ethnicity_collapsedOther ethnic group | 1.932 | 1.425 | 0.462 | 0.644 | 0.118 | 31.562 | |
| outsourcing_statusOutsourced:Ethnicity_collapsedWhite other | 1.004 | 0.397 | 0.009 | 0.993 | 0.461 | 2.184 | |
We explored the interaction effect using targeted contrasts comparing
- The effect of outsourcing within each level of ethnicity
- The effect of each ethnicity versus “White British” within each level of outsourcing
We do not consider contrasts for which any cell count is less than 10.
3.4.3.6.2.1 Post-hoc: Outsourcing within ethnicity
- White British workers are 1.2290804 times as likely to be in the Mid group if they are outsourced compared to not-outsourced
- White British workers are 1.8204336 times as likely to be in the Low group if they are outsourced compared to not-outsourced
- White British workers are 0.6683549 times as likely to be in the High group if they are outsourced compared to not-outsourced
- Mixed/Multiple ethnic group workers are 6.0698533 times as likely to be in the Low group if they are outsourced compared to not-outsourced (note large confidence interval for this effect - see plot)
In essence, a White British person is more likely to be in the low or mid income group, and less likely to be in the high income group, if they are outsourced compared to not-outsourced.
3.4.3.6.2.2 Post-hoc: Ethnicity within outsourcing
Comparing ethnicities within outsourcing status revealed no significant contrasts.
The plot below of predicted probabilities suggests that non-White ethnicities are not more or less likely to be in the low income group regardless of the outsourcing status, compared to White British. That is, the lack of an effect of outsourcing status on low income group membership does not appear to be attributable to a higher likelihood generally of marginalised ethnicities being low paid.
3.4.3.6.3 Ethnicity 21
A model including the ethnicity:outsourcing interaction term significantly improved model fit compared to a model without the interaction term, X^2(38) = 2276.50414, p < .001. The table below shows the model coefficients.
| income_group | estimate | std.error | statistic | p.value | conf.low | conf.high | sig |
|---|---|---|---|---|---|---|---|
| Mid | |||||||
| (Intercept) | 0.130 | 0.173 | -11.796 | 0.000 | 0.093 | 0.182 | *** |
| outsourcing_statusOutsourced | 1.428 | 0.102 | 3.479 | 0.001 | 1.168 | 1.746 | *** |
| Ethnicity_collapsed_disaggregatedIrish | 1.629 | 0.300 | 1.628 | 0.103 | 0.905 | 2.932 | |
| Ethnicity_collapsed_disaggregatedGypsy or Irish Traveller | 0.295 | 1.073 | -1.137 | 0.256 | 0.036 | 2.420 | |
| Ethnicity_collapsed_disaggregatedRoma | 0.000 | 0.308 | -51.946 | 0.000 | 0.000 | 0.000 | *** |
| Ethnicity_collapsed_disaggregatedAny other White background | 1.090 | 0.231 | 0.372 | 0.710 | 0.692 | 1.716 | |
| Ethnicity_collapsed_disaggregatedWhite and Black Caribbean | 1.097 | 0.377 | 0.245 | 0.806 | 0.524 | 2.297 | |
| Ethnicity_collapsed_disaggregatedWhite and Black African | 0.711 | 0.585 | -0.583 | 0.560 | 0.226 | 2.236 | |
| Ethnicity_collapsed_disaggregatedWhite and Asian | 1.085 | 0.381 | 0.215 | 0.830 | 0.514 | 2.291 | |
| Ethnicity_collapsed_disaggregatedAny other Mixed / Multiple ethnic background | 1.235 | 0.385 | 0.549 | 0.583 | 0.581 | 2.629 | |
| Ethnicity_collapsed_disaggregatedIndian | 0.895 | 0.269 | -0.415 | 0.678 | 0.528 | 1.514 | |
| Ethnicity_collapsed_disaggregatedPakistani | 1.241 | 0.314 | 0.690 | 0.490 | 0.672 | 2.295 | |
| Ethnicity_collapsed_disaggregatedBangladeshi | 1.491 | 0.449 | 0.889 | 0.374 | 0.618 | 3.597 | |
| Ethnicity_collapsed_disaggregatedChinese | 0.631 | 0.408 | -1.128 | 0.259 | 0.283 | 1.404 | |
| Ethnicity_collapsed_disaggregatedAny other Asian background | 1.628 | 0.405 | 1.205 | 0.228 | 0.737 | 3.599 | |
| Ethnicity_collapsed_disaggregatedAfrican | 1.297 | 0.243 | 1.070 | 0.285 | 0.806 | 2.089 | |
| Ethnicity_collapsed_disaggregatedCaribbean | 1.074 | 0.404 | 0.177 | 0.860 | 0.486 | 2.372 | |
| Ethnicity_collapsed_disaggregatedAny other Black, Black British, or Caribbean background | 2.481 | 0.479 | 1.897 | 0.058 | 0.970 | 6.346 | |
| Ethnicity_collapsed_disaggregatedArab | 1.804 | 0.832 | 0.710 | 0.478 | 0.354 | 9.208 | |
| Ethnicity_collapsed_disaggregatedAny other ethnic group | 0.000 | 0.274 | -55.213 | 0.000 | 0.000 | 0.000 | *** |
| Ethnicity_collapsed_disaggregatedDon’t think of myself as any of these | 0.635 | 1.095 | -0.415 | 0.678 | 0.074 | 5.432 | |
| Age | 1.008 | 0.003 | 2.867 | 0.004 | 1.002 | 1.013 | ** |
| GenderFemale | 2.783 | 0.071 | 14.373 | 0.000 | 2.421 | 3.200 | *** |
| GenderOther | 4.045 | 0.668 | 2.093 | 0.036 | 1.093 | 14.972 | * |
| GenderPrefer not to say | 2.010 | 0.847 | 0.825 | 0.410 | 0.382 | 10.571 | |
| Has_DegreeNo | 1.931 | 0.067 | 9.822 | 0.000 | 1.694 | 2.202 | *** |
| Has_DegreeDon't know | 3.200 | 0.306 | 3.800 | 0.000 | 1.756 | 5.830 | *** |
| RegionEast Midlands | 1.098 | 0.153 | 0.609 | 0.543 | 0.813 | 1.482 | |
| RegionEast of England | 1.090 | 0.158 | 0.544 | 0.586 | 0.799 | 1.487 | |
| RegionNorth East | 0.816 | 0.193 | -1.053 | 0.292 | 0.558 | 1.192 | |
| RegionNorth West | 0.727 | 0.146 | -2.174 | 0.030 | 0.546 | 0.969 | * |
| RegionNorthern Ireland | 1.071 | 0.214 | 0.321 | 0.749 | 0.704 | 1.629 | |
| RegionScotland | 1.112 | 0.158 | 0.673 | 0.501 | 0.816 | 1.517 | |
| RegionSouth East | 0.965 | 0.134 | -0.264 | 0.792 | 0.742 | 1.255 | |
| RegionSouth West | 0.878 | 0.149 | -0.876 | 0.381 | 0.656 | 1.175 | |
| RegionWales | 0.645 | 0.196 | -2.245 | 0.025 | 0.439 | 0.946 | * |
| RegionWest Midlands | 0.896 | 0.146 | -0.748 | 0.455 | 0.673 | 1.194 | |
| RegionYorkshire and the Humber | 0.904 | 0.150 | -0.675 | 0.500 | 0.673 | 1.213 | |
| BORNUK_labelledWithin the last year | 1.537 | 0.277 | 1.550 | 0.121 | 0.892 | 2.647 | |
| BORNUK_labelledWithin the last 3 years | 0.987 | 0.261 | -0.051 | 0.960 | 0.592 | 1.645 | |
| BORNUK_labelledWithin the last 5 years | 1.017 | 0.285 | 0.058 | 0.953 | 0.582 | 1.777 | |
| BORNUK_labelledWithin the last 10 years | 0.895 | 0.255 | -0.435 | 0.664 | 0.544 | 1.474 | |
| BORNUK_labelledWithin the last 15 years | 0.886 | 0.286 | -0.424 | 0.672 | 0.506 | 1.551 | |
| BORNUK_labelledWithin the last 20 years | 0.865 | 0.264 | -0.549 | 0.583 | 0.515 | 1.452 | |
| BORNUK_labelledWithin the last 30 years | 0.545 | 0.454 | -1.337 | 0.181 | 0.224 | 1.327 | |
| BORNUK_labelledMore than 30 years ago | 0.870 | 0.275 | -0.508 | 0.612 | 0.508 | 1.490 | |
| BORNUK_labelledPrefer not to say | 2.168 | 0.435 | 1.778 | 0.075 | 0.924 | 5.088 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedIrish | 0.273 | 0.867 | -1.497 | 0.134 | 0.050 | 1.494 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedGypsy or Irish Traveller | 0.000 | 1.527 | -8.518 | 0.000 | 0.000 | 0.000 | *** |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedRoma | 1.634 | 0.440 | 1.117 | 0.264 | 0.690 | 3.868 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other White background | 0.827 | 0.369 | -0.515 | 0.607 | 0.401 | 1.705 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedWhite and Black Caribbean | 2.145 | 0.820 | 0.931 | 0.352 | 0.430 | 10.701 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedWhite and Black African | 1.616 | 0.921 | 0.521 | 0.602 | 0.266 | 9.824 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedWhite and Asian | 20.168 | 1.116 | 2.691 | 0.007 | 2.262 | 179.829 | ** |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other Mixed / Multiple ethnic background | 3.586 | 0.769 | 1.661 | 0.097 | 0.795 | 16.184 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedIndian | 0.768 | 0.480 | -0.550 | 0.582 | 0.300 | 1.966 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedPakistani | 1.066 | 0.502 | 0.127 | 0.899 | 0.399 | 2.849 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedBangladeshi | 1.261 | 0.811 | 0.286 | 0.775 | 0.257 | 6.176 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedChinese | 1.755 | 0.949 | 0.593 | 0.553 | 0.273 | 11.271 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other Asian background | 0.255 | 0.705 | -1.940 | 0.052 | 0.064 | 1.014 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAfrican | 0.438 | 0.391 | -2.107 | 0.035 | 0.203 | 0.944 | * |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedCaribbean | 0.576 | 1.066 | -0.518 | 0.605 | 0.071 | 4.655 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other Black, Black British, or Caribbean background | 0.044 | 1.165 | -2.674 | 0.007 | 0.005 | 0.435 | ** |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedArab | 0.701 | 1.405 | -0.252 | 0.801 | 0.045 | 11.011 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other ethnic group | 21564953.458 | 1.498 | 11.274 | 0.000 | 1144826.512 | 406216324.123 | *** |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedDon’t think of myself as any of these | 0.296 | 1.581 | -0.770 | 0.441 | 0.013 | 6.560 | |
| High | |||||||
| (Intercept) | 0.519 | 0.159 | -4.133 | 0.000 | 0.380 | 0.708 | *** |
| outsourcing_statusOutsourced | 0.637 | 0.109 | -4.143 | 0.000 | 0.514 | 0.788 | *** |
| Ethnicity_collapsed_disaggregatedIrish | 0.652 | 0.323 | -1.323 | 0.186 | 0.346 | 1.229 | |
| Ethnicity_collapsed_disaggregatedGypsy or Irish Traveller | 0.000 | 0.271 | -57.918 | 0.000 | 0.000 | 0.000 | *** |
| Ethnicity_collapsed_disaggregatedRoma | 0.000 | 0.304 | -51.196 | 0.000 | 0.000 | 0.000 | *** |
| Ethnicity_collapsed_disaggregatedAny other White background | 1.110 | 0.190 | 0.550 | 0.583 | 0.765 | 1.612 | |
| Ethnicity_collapsed_disaggregatedWhite and Black Caribbean | 0.936 | 0.395 | -0.167 | 0.868 | 0.431 | 2.032 | |
| Ethnicity_collapsed_disaggregatedWhite and Black African | 0.736 | 0.471 | -0.649 | 0.516 | 0.292 | 1.855 | |
| Ethnicity_collapsed_disaggregatedWhite and Asian | 0.998 | 0.357 | -0.005 | 0.996 | 0.496 | 2.008 | |
| Ethnicity_collapsed_disaggregatedAny other Mixed / Multiple ethnic background | 0.701 | 0.365 | -0.971 | 0.331 | 0.343 | 1.435 | |
| Ethnicity_collapsed_disaggregatedIndian | 1.209 | 0.229 | 0.828 | 0.408 | 0.771 | 1.895 | |
| Ethnicity_collapsed_disaggregatedPakistani | 0.508 | 0.325 | -2.083 | 0.037 | 0.269 | 0.961 | * |
| Ethnicity_collapsed_disaggregatedBangladeshi | 0.532 | 0.489 | -1.291 | 0.197 | 0.204 | 1.387 | |
| Ethnicity_collapsed_disaggregatedChinese | 0.990 | 0.334 | -0.030 | 0.976 | 0.514 | 1.906 | |
| Ethnicity_collapsed_disaggregatedAny other Asian background | 0.894 | 0.417 | -0.267 | 0.789 | 0.395 | 2.027 | |
| Ethnicity_collapsed_disaggregatedAfrican | 0.730 | 0.246 | -1.278 | 0.201 | 0.450 | 1.183 | |
| Ethnicity_collapsed_disaggregatedCaribbean | 1.326 | 0.327 | 0.864 | 0.388 | 0.699 | 2.516 | |
| Ethnicity_collapsed_disaggregatedAny other Black, Black British, or Caribbean background | 0.729 | 0.538 | -0.587 | 0.557 | 0.254 | 2.093 | |
| Ethnicity_collapsed_disaggregatedArab | 2.997 | 0.773 | 1.419 | 0.156 | 0.658 | 13.644 | |
| Ethnicity_collapsed_disaggregatedAny other ethnic group | 1.818 | 0.549 | 1.088 | 0.277 | 0.619 | 5.334 | |
| Ethnicity_collapsed_disaggregatedDon’t think of myself as any of these | 0.000 | 0.292 | -53.603 | 0.000 | 0.000 | 0.000 | *** |
| Age | 1.010 | 0.002 | 4.100 | 0.000 | 1.005 | 1.015 | *** |
| GenderFemale | 0.489 | 0.065 | -11.013 | 0.000 | 0.430 | 0.555 | *** |
| GenderOther | 1.105 | 0.882 | 0.114 | 0.909 | 0.196 | 6.226 | |
| GenderPrefer not to say | 0.959 | 0.711 | -0.058 | 0.953 | 0.238 | 3.862 | |
| Has_DegreeNo | 0.298 | 0.070 | -17.260 | 0.000 | 0.260 | 0.342 | *** |
| Has_DegreeDon't know | 0.495 | 0.350 | -2.014 | 0.044 | 0.249 | 0.981 | * |
| RegionEast Midlands | 1.646 | 0.158 | 3.157 | 0.002 | 1.208 | 2.243 | ** |
| RegionEast of England | 1.888 | 0.161 | 3.956 | 0.000 | 1.378 | 2.587 | *** |
| RegionNorth East | 1.577 | 0.190 | 2.400 | 0.016 | 1.087 | 2.287 | * |
| RegionNorth West | 1.532 | 0.146 | 2.921 | 0.003 | 1.151 | 2.040 | ** |
| RegionNorthern Ireland | 1.761 | 0.241 | 2.344 | 0.019 | 1.097 | 2.825 | * |
| RegionScotland | 1.012 | 0.164 | 0.075 | 0.940 | 0.735 | 1.395 | |
| RegionSouth East | 1.445 | 0.142 | 2.598 | 0.009 | 1.095 | 1.907 | ** |
| RegionSouth West | 1.248 | 0.158 | 1.406 | 0.160 | 0.916 | 1.700 | |
| RegionWales | 1.258 | 0.180 | 1.279 | 0.201 | 0.885 | 1.789 | |
| RegionWest Midlands | 1.475 | 0.156 | 2.486 | 0.013 | 1.086 | 2.005 | * |
| RegionYorkshire and the Humber | 1.334 | 0.162 | 1.776 | 0.076 | 0.971 | 1.834 | |
| BORNUK_labelledWithin the last year | 0.354 | 0.367 | -2.830 | 0.005 | 0.173 | 0.727 | ** |
| BORNUK_labelledWithin the last 3 years | 0.516 | 0.278 | -2.381 | 0.017 | 0.299 | 0.890 | * |
| BORNUK_labelledWithin the last 5 years | 0.792 | 0.261 | -0.893 | 0.372 | 0.476 | 1.320 | |
| BORNUK_labelledWithin the last 10 years | 0.738 | 0.227 | -1.341 | 0.180 | 0.473 | 1.151 | |
| BORNUK_labelledWithin the last 15 years | 0.928 | 0.256 | -0.291 | 0.771 | 0.562 | 1.534 | |
| BORNUK_labelledWithin the last 20 years | 1.051 | 0.263 | 0.191 | 0.849 | 0.628 | 1.760 | |
| BORNUK_labelledWithin the last 30 years | 0.672 | 0.331 | -1.204 | 0.229 | 0.351 | 1.284 | |
| BORNUK_labelledMore than 30 years ago | 1.012 | 0.229 | 0.051 | 0.960 | 0.646 | 1.583 | |
| BORNUK_labelledPrefer not to say | 1.483 | 0.554 | 0.711 | 0.477 | 0.501 | 4.391 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedIrish | 0.417 | 1.182 | -0.739 | 0.460 | 0.041 | 4.231 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedGypsy or Irish Traveller | 128775445.181 | 1.823 | 10.243 | 0.000 | 3614467.824 | 4587982543.925 | *** |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedRoma | 0.522 | 0.428 | -1.520 | 0.129 | 0.225 | 1.207 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other White background | 0.901 | 0.412 | -0.253 | 0.800 | 0.402 | 2.020 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedWhite and Black Caribbean | 0.000 | 0.536 | -26.297 | 0.000 | 0.000 | 0.000 | *** |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedWhite and Black African | 1.455 | 0.762 | 0.492 | 0.623 | 0.327 | 6.481 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedWhite and Asian | 14.464 | 1.228 | 2.176 | 0.030 | 1.303 | 160.512 | * |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other Mixed / Multiple ethnic background | 3.592 | 0.805 | 1.588 | 0.112 | 0.741 | 17.404 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedIndian | 0.922 | 0.443 | -0.182 | 0.855 | 0.387 | 2.198 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedPakistani | 0.796 | 0.748 | -0.305 | 0.761 | 0.184 | 3.450 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedBangladeshi | 4.675 | 0.859 | 1.796 | 0.073 | 0.868 | 25.166 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedChinese | 3.235 | 0.795 | 1.477 | 0.140 | 0.681 | 15.373 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other Asian background | 0.395 | 0.794 | -1.172 | 0.241 | 0.083 | 1.869 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAfrican | 1.527 | 0.358 | 1.183 | 0.237 | 0.757 | 3.080 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedCaribbean | 0.863 | 0.707 | -0.208 | 0.835 | 0.216 | 3.450 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other Black, Black British, or Caribbean background | 1.543 | 0.838 | 0.517 | 0.605 | 0.299 | 7.969 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedArab | 0.357 | 1.402 | -0.734 | 0.463 | 0.023 | 5.576 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other ethnic group | 1.866 | 1.429 | 0.437 | 0.662 | 0.113 | 30.695 | |
| outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedDon’t think of myself as any of these | 1.993 | 0.424 | 1.627 | 0.104 | 0.868 | 4.576 | |
3.4.3.6.3.1 Post-hoc: Outsourcing within ethnicity
- White and Black Caribbean workers are 0.0000004 times as likely to be in the High group if they are outsourced compared to not-outsourced
- English / Welsh / Scottish / Northern Irish / British workers are 1.2271883 times as likely to be in the Mid group if they are outsourced compared to not-outsourced
- English / Welsh / Scottish / Northern Irish / British workers are 1.819074 times as likely to be in the Low group if they are outsourced compared to not-outsourced
- English / Welsh / Scottish / Northern Irish / British workers are 0.6632549 times as likely to be in the High group if they are outsourced compared to not-outsourced
3.4.3.6.3.2 Post-hoc: Ethnicity within outsourcing
Comparing ethnicities within outsourcing status,
- Among Not outsourced workers, people of Irish ethnicity are 2.7221653 times as likely to be in the Low group compared to English / Welsh / Scottish / Northern Irish / British people.
As for the aggregated model, there is no evidence to suggest that the lack of a difference for ethnic minorities is due to a higher likelihood of being low paid regardless of outsourcing status.
4 Analysis - Study 2
5 Study 2 Overview
Analysis from Study 2 appearing in [NAME OF REPORT] primarily employs a descriptive approach to understand the data. We conducted several cross-tabulations focusing on key demographic variables including Migration Status, Low Pay, and Ethnicity.
Due to the extensive number of variables examined, these cross-tabulations are not reproduced in this document. However, researchers can easily recreate these analyses by running the “Crosstabulations.qmd” script available in the GitHub repository associated with this project (see @reproducibility). The repository contains all necessary data files and code to replicate our findings.
For brevity this document only focuses on the findings which are included in the report (and/or closely related)
Data used for these analysis can be reproduced by runing the data cleaning file in the repository [link/name]. The data was then split into two datasets, one containing income outliers and one with income outliers removed. The no outliers dataset is used in any analyses which include pay related variables. The outlier exclusion criteria are the same as those in Study 1. In this dataset 10.1% (183) cases are removed in the no outlier dataset.
Data quality check - Income outlier filtering:
Original dataset: 1814 rows
After removing income outliers: 1631 rows
Income outliers removed: 183 ( 10.1 %)
5.1 Pay Comparison
First we explore subjective perceptions of Pay, where participants selected whether they believed they were paid more or less than in-house workers. The analysis is conducted using simple crosstabulations across key demographic variables; Sex, Age, Eethnicity, Region, Income group (e.g. High, Low or Middle), Education Band (High, Low, Middle) and Place of Birth (UK, Not UK)
label | variable | Pros_And_Cons_Pay | Total | |||
|---|---|---|---|---|---|---|
Don't know | I get paid less | I get paid more | Neither / no impact | |||
Sex | Male | 68 (7.42%) | 170 (18.56%) | 291 (31.77%) | 387 (42.25%) | 916 (56.16%) |
Female | 77 (10.81%) | 111 (15.59%) | 152 (21.35%) | 372 (52.25%) | 712 (43.65%) | |
Other | 0 (0%) | 0 (0%) | 1 (100.00%) | 0 (0%) | 1 (0.06%) | |
Prefer not to say | 2 (100.00%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (0.12%) | |
Total | 147 (9.01%) | 281 (17.23%) | 444 (27.22%) | 759 (46.54%) | 1631 (100.00%) | |
Age | median | 39.0 | 38.0 | 33.0 | 40.0 | 37.0 |
mean | 39.8 | 38.5 | 35.7 | 40.9 | 39.0 | |
std dev | 13.2 | 12.8 | 11.9 | 13.5 | 13.1 | |
Ethnicity | African | 13 (7.93%) | 27 (16.46%) | 52 (31.71%) | 72 (43.90%) | 164 (10.16%) |
Any other Asian background | 0 (0%) | 4 (16.67%) | 13 (54.17%) | 7 (29.17%) | 24 (1.49%) | |
Any other Black, Black British, or Caribbean background | 1 (7.14%) | 6 (42.86%) | 3 (21.43%) | 4 (28.57%) | 14 (0.87%) | |
Any other ethnic group | 1 (33.33%) | 0 (0%) | 2 (66.67%) | 0 (0%) | 3 (0.19%) | |
Any other Mixed / Multiple ethnic background | 1 (14.29%) | 2 (28.57%) | 0 (0%) | 4 (57.14%) | 7 (0.43%) | |
Any other White background | 7 (8.75%) | 15 (18.75%) | 23 (28.75%) | 35 (43.75%) | 80 (4.96%) | |
Arab | 1 (14.29%) | 1 (14.29%) | 3 (42.86%) | 2 (28.57%) | 7 (0.43%) | |
Bangladeshi | 2 (11.76%) | 3 (17.65%) | 6 (35.29%) | 6 (35.29%) | 17 (1.05%) | |
Caribbean | 5 (20.83%) | 1 (4.17%) | 9 (37.50%) | 9 (37.50%) | 24 (1.49%) | |
Chinese | 1 (7.14%) | 3 (21.43%) | 2 (14.29%) | 8 (57.14%) | 14 (0.87%) | |
Don’t think of myself as any of these | 1 (100.00%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.06%) | |
English / Welsh / Scottish / Northern Irish / British | 94 (8.79%) | 176 (16.45%) | 274 (25.61%) | 526 (49.16%) | 1070 (66.29%) | |
Gypsy or Irish Traveller | 0 (0%) | 0 (0%) | 2 (100.00%) | 0 (0%) | 2 (0.12%) | |
Indian | 5 (9.09%) | 12 (21.82%) | 9 (16.36%) | 29 (52.73%) | 55 (3.41%) | |
Irish | 0 (0%) | 6 (37.50%) | 1 (6.25%) | 9 (56.25%) | 16 (0.99%) | |
Pakistani | 5 (13.16%) | 7 (18.42%) | 14 (36.84%) | 12 (31.58%) | 38 (2.35%) | |
Prefer not to say | 2 (40.00%) | 1 (20.00%) | 0 (0%) | 2 (40.00%) | 5 (0.31%) | |
Roma | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100.00%) | 2 (0.12%) | |
White and Asian | 1 (6.67%) | 3 (20.00%) | 3 (20.00%) | 8 (53.33%) | 15 (0.93%) | |
White and Black African | 1 (3.12%) | 6 (18.75%) | 16 (50.00%) | 9 (28.12%) | 32 (1.98%) | |
White and Black Caribbean | 2 (8.33%) | 6 (25.00%) | 6 (25.00%) | 10 (41.67%) | 24 (1.49%) | |
Total | 143 (8.86%) | 279 (17.29%) | 438 (27.14%) | 754 (46.72%) | 1614 (100.00%) | |
Ethnicity_Collapsed | White British | 94 (8.79%) | 176 (16.45%) | 274 (25.61%) | 526 (49.16%) | 1070 (66.34%) |
Arab | 1 (14.29%) | 1 (14.29%) | 3 (42.86%) | 2 (28.57%) | 7 (0.43%) | |
Asian/Asian British | 13 (8.78%) | 29 (19.59%) | 44 (29.73%) | 62 (41.89%) | 148 (9.18%) | |
Black/African/Caribbean/Black British | 19 (9.41%) | 34 (16.83%) | 64 (31.68%) | 85 (42.08%) | 202 (12.52%) | |
Mixed/Multiple ethnic groups | 5 (6.41%) | 17 (21.79%) | 25 (32.05%) | 31 (39.74%) | 78 (4.84%) | |
Other ethnic group | 1 (33.33%) | 0 (0%) | 2 (66.67%) | 0 (0%) | 3 (0.19%) | |
Prefer not to say | 2 (40.00%) | 1 (20.00%) | 0 (0%) | 2 (40.00%) | 5 (0.31%) | |
White Other | 7 (7.00%) | 21 (21.00%) | 26 (26.00%) | 46 (46.00%) | 100 (6.20%) | |
Total | 142 (8.80%) | 279 (17.30%) | 438 (27.15%) | 754 (46.75%) | 1613 (100.00%) | |
Region | London | 26 (9.42%) | 47 (17.03%) | 92 (33.33%) | 111 (40.22%) | 276 (16.92%) |
East Midlands | 10 (7.81%) | 18 (14.06%) | 33 (25.78%) | 67 (52.34%) | 128 (7.85%) | |
East of England | 15 (11.19%) | 30 (22.39%) | 34 (25.37%) | 55 (41.04%) | 134 (8.22%) | |
North East | 5 (7.25%) | 13 (18.84%) | 19 (27.54%) | 32 (46.38%) | 69 (4.23%) | |
North West | 20 (9.43%) | 28 (13.21%) | 62 (29.25%) | 102 (48.11%) | 212 (13.00%) | |
Northern Ireland | 2 (7.14%) | 7 (25.00%) | 4 (14.29%) | 15 (53.57%) | 28 (1.72%) | |
Scotland | 5 (4.81%) | 24 (23.08%) | 24 (23.08%) | 51 (49.04%) | 104 (6.38%) | |
South East | 21 (9.29%) | 39 (17.26%) | 53 (23.45%) | 113 (50.00%) | 226 (13.86%) | |
South West | 14 (11.02%) | 24 (18.90%) | 32 (25.20%) | 57 (44.88%) | 127 (7.79%) | |
Wales | 4 (6.15%) | 12 (18.46%) | 18 (27.69%) | 31 (47.69%) | 65 (3.99%) | |
West Midlands | 14 (9.79%) | 25 (17.48%) | 48 (33.57%) | 56 (39.16%) | 143 (8.77%) | |
Yorkshire and the Humber | 11 (9.24%) | 14 (11.76%) | 25 (21.01%) | 69 (57.98%) | 119 (7.30%) | |
Total | 147 (9.01%) | 281 (17.23%) | 444 (27.22%) | 759 (46.54%) | 1631 (100.00%) | |
income_group | Mid | 60 (7.22%) | 159 (19.13%) | 235 (28.28%) | 377 (45.37%) | 831 (53.27%) |
High | 17 (5.06%) | 49 (14.58%) | 110 (32.74%) | 160 (47.62%) | 336 (21.54%) | |
Low | 49 (12.47%) | 67 (17.05%) | 89 (22.65%) | 188 (47.84%) | 393 (25.19%) | |
Total | 126 (8.08%) | 275 (17.63%) | 434 (27.82%) | 725 (46.47%) | 1560 (100.00%) | |
Education_Band | High | 73 (7.53%) | 173 (17.84%) | 315 (32.47%) | 409 (42.16%) | 970 (59.47%) |
Low | 30 (18.63%) | 23 (14.29%) | 26 (16.15%) | 82 (50.93%) | 161 (9.87%) | |
Mid | 44 (8.80%) | 85 (17.00%) | 103 (20.60%) | 268 (53.60%) | 500 (30.66%) | |
Total | 147 (9.01%) | 281 (17.23%) | 444 (27.22%) | 759 (46.54%) | 1631 (100.00%) | |
BORNUK_binary | Born in UK | 100 (8.83%) | 182 (16.06%) | 283 (24.98%) | 568 (50.13%) | 1133 (69.47%) |
Not born in UK | 36 (7.86%) | 97 (21.18%) | 153 (33.41%) | 172 (37.55%) | 458 (28.08%) | |
Prefer not to say | 11 (27.50%) | 2 (5.00%) | 8 (20.00%) | 19 (47.50%) | 40 (2.45%) | |
Total | 147 (9.01%) | 281 (17.23%) | 444 (27.22%) | 759 (46.54%) | 1631 (100.00%) | |
The pay comparison analysis reveals several notable demographic patterns in perceived pay inequality among outsourced workers. Sex differences are evident, with men more likely to report being paid more than in-house workers (31.77%) compared to women (21.35%), while women more frequently report no pay difference (52.25% vs 42.25%). Age patterns show that those reporting higher pay tend to be younger (mean age 35.7 years) compared to those reporting lower pay (mean age 38.5 years). Education disparities are striking, with highly educated workers much more likely to report being paid more (32.47%) compared to those with low education (16.15%). Place of birth differences emerge, with workers not born in the UK more likely to report being paid more (33.41%) than UK-born workers (24.98%), though they also report higher rates of being paid less (21.18% vs 16.06%). Income group patterns show that high-income workers are most likely to report being paid more as expected (32.74%), while low-income workers show the highest rates of uncertainty about their pay situation (12.47% “don’t know”).
5.2 Work Preferences
Next we explore simple counts/percentages of Outsourced workers preferences for in-house vs outsourced work.
Work Preference | Count | Percentage |
|---|---|---|
I have no preference | 718 | 39.6 |
I would prefer to be an in-house worker | 287 | 15.8 |
I would prefer to be an outsourced worker | 199 | 11.0 |
I would strongly prefer to be an in-house worker | 295 | 16.3 |
I would strongly prefer to be an outsourced worker | 126 | 6.9 |
Not sure | 189 | 10.4 |
The work preferences data show that the largest group of outsourced workers (39.6%) express no preference between in-house and outsourced employment. However, among those with preferences, there is a clear preference for in-house work, with 32.1% preferring in-house positions (16.3% strongly + 15.8% prefer) compared to 17.9% preferring outsourced work (6.9% strongly + 11.0% prefer).
5.3 Job Motivation
Here we examine the motivational factors that influence outsourced workers’ decisions to remain in their current roles. Using a descriptive approach, we present the frequency and percentage distribution of responses across eleven distinct motivational categories, ranging from intrinsic factors (job satisfaction, workplace culture) to extrinsic factors (pay, location convenience) and personal circumstances (health conditions, caregiving responsibilities).
Reason for Current Role | Count | Percentage |
|---|---|---|
My job is in a convenient location | 748 | 41.2 |
The pay is good | 739 | 40.7 |
I like doing this kind of work | 728 | 40.1 |
I like my colleagues | 652 | 35.9 |
I can work flexibly in a way which suits me | 581 | 32.0 |
I like the workplace culture | 502 | 27.7 |
This was the best job available to me | 437 | 24.1 |
It is helping me develop skills and experience I need to progress | 428 | 23.6 |
I can do the job alongside childcare or caring for others | 264 | 14.6 |
I can do the job alongside managing my health conditions | 235 | 13.0 |
I do not have the formal qualifications I need to do another job I would prefer | 163 | 9.0 |
The job motivation analysis shows the key factors driving outsourced workers’ employment decisions. Practical considerations dominate, with job location convenience being the most frequently cited reason (41.2%), followed closely by pay satisfaction (40.7%) and intrinsic job satisfaction (40.1%). Social factors also play a significant role, with over one-third (35.9%) citing positive relationships with colleagues. Flexibility, often assumed to be a primary motivation for outsourced work, ranks fifth at 32.0%. Workplace culture is important to just over a quarter (27.7%) of workers. Necessity-driven motivations are less common but notable, with 24.1% stating this was the best job available and 23.6% viewing it as a stepping stone for skill development. Personal circumstances account for smaller proportions: 14.6% balance the job with caregiving responsibilities, 13.0% manage health conditions, and only 9.0% remain due to qualification constraints (although it is possible that social desirability is playing a role in participants willingness to respond to questions about their level of qualifications and its relation to their employment possibilities).
5.4 Pros and Cons of Outsourced Work
Here we explore outsourced workers’ comparative assessments of their employment conditions relative to hypothetical in-house positions. Participants rated fourteen distinct workplace dimensions on a scale ranging from “less/worse” to “more/better” compared to in-house workers.
The pros and cons suggests areas of disadvantage for outsourced workers compared to hypothetical in-house positions. Career development emerges as the most problematic area, with 24% reporting worse opportunities for progression/promotion and 20% citing reduced access to training and development. Job security concerns are similarly prominent, with 20% reporting worse access to job security. Workplace relationships and engagement show notable deficits, with 19% feeling less connected to colleagues and 17% feeling less invested in their role. Workers’ rights and voice present challenges, with 19% finding it harder to assert rights at work.
Flexibility stands out as a key advantage, with 41.8% reporting better flexibility compared to only 14.2% reporting worse flexibility - making it the strongest positive aspect of outsourced work. Pay perceptions are mixed, with 17% reporting worse pay, though this varies significantly by demographic group as shown in the earlier analysis.
The overall pattern shows that while outsourced workers may benefit from increased flexibility, they face systematic disadvantages in career development, job security, workplace relationships, and employee voice. Most concerning is that career progression and training opportunities - crucial for long-term economic mobility - rank as the most problematic areas for outsourced workers.
5.5 Cumulative Burden
Here we quantify the cumulative burden of negative work experiences among outsourced workers by transforming the categorical responses from the pros and cons analysis into numerical scores (-1 for negative, 0 for neutral, +1 for positive). We calculate the total number of negative outcomes per respondent across all 14 workplace dimensions and examine the distribution of these counts within the sample. The analysis includes a focused examination of workers who report being paid less than in-house colleagues, investigating whether pay disadvantage is associated with broader patterns of workplace disadvantage.
Number of Negative Outcomes | Count | Percentage |
|---|---|---|
No negative impacts | 699 | 38.5 |
1-2 negative outcomes | 393 | 21.7 |
3-4 negative outcomes | 294 | 16.2 |
5+ negative outcomes | 428 | 23.6 |
The negative Impacts analysis reveals the cumulative burden of workplace disadvantages among outsourced workers. The distribution shows significant polarisation: 38.5% of workers report no negative outcomes, while 23.6% experience five or more negative outcomes across the fourteen workplace dimensions (mean = 2.51, median = 1).
Most striking is the relationship between pay disadvantage and cumulative negative outcomes. Among workers who report being paid less than in-house colleagues, 96.4% experience at least one additional negative outcome beyond pay - only 3.6% report pay disadvantage as an isolated issue. The concentration of negative outcomes among this group is severe: 69.7% experience five or more total negative outcomes (including pay) compared to just 23.6% in the overall population - nearly a threefold difference.
The pattern suggests that pay disadvantage rarely occurs in isolation but is typically accompanied by broader workplace disadvantages. Workers reporting pay disadvantage show dramatically higher rates of multiple negative outcomes (10.1% have 4+ negatives including pay vs 7.4% overall), indicating that pay inequity is a marker of comprehensive workplace disadvantage rather than an isolated issue.
5.6 Statistical Analysis
Continuing the analysis of pros and cons of outsourced working this section presents descriptive analysis of mean negative outcomes across demographic groups, followed by regression modeling to identify predictors. The analysis compares Poisson, linear, and negative binomial regression models using fit statistics (AIC, deviance, RMSE) to select the best-fitting model. Results are presented through a model comparison table, coefficients table with rate ratios and confidence intervals, forest plot visualisation of rate ratios, and predicted values for different income groups. The analysis identifies which demographic characteristics are associated with higher rates of negative workplace outcomes among outsourced workers.
The statistical analysis reveals significant demographic predictors of negative workplace outcomes among outsourced workers. Model selection favoured negative binomial regression due to overdispersion (ratio = 3.387), with this model showing the best fit (AIC = 6519.5) compared to Poisson (AIC = 8173.4) and linear models (AIC = 7700.3).
Overdispersion ratio: 3.387
If > 1.5, consider negative binomial or quasi-Poisson
Negative binomial model fitted due to overdispersion
Model Type | AIC | Deviance | R-Squared | RMSE |
|---|---|---|---|---|
Full Poisson | 8,173.415 | 5,128.115 | 1.822 | |
Full Linear | 7,700.326 | 0.07 | 2.871 | |
Full Negative Binomial | 6,519.478 | 1,690.507 | 1.046 |
Variable | Coefficient | Std Error | Rate Ratio | 95% CI Lower | 95% CI Upper | P-Value | Significant |
|---|---|---|---|---|---|---|---|
income_groupHigh | -0.2529 | 0.0870 | 0.7765 | -0.4233 | -0.0808 | 0.0037 | TRUE |
income_groupLow | 0.1055 | 0.0823 | 1.1112 | -0.0578 | 0.2706 | 0.2002 | FALSE |
Ethnicity_CollapsedArab | -0.3962 | 0.5069 | 0.6729 | -1.3302 | 0.6882 | 0.4345 | FALSE |
Ethnicity_CollapsedAsian/Asian British | 0.0185 | 0.1248 | 1.0186 | -0.2230 | 0.2658 | 0.8823 | FALSE |
Ethnicity_CollapsedBlack/African/Caribbean/Black British | -0.2726 | 0.1196 | 0.7614 | -0.5034 | -0.0391 | 0.0226 | TRUE |
Ethnicity_CollapsedMixed/Multiple ethnic groups | -0.0598 | 0.1577 | 0.9419 | -0.3632 | 0.2566 | 0.7045 | FALSE |
Ethnicity_CollapsedOther ethnic group | -0.6512 | 0.7920 | 0.5214 | -2.0656 | 1.1238 | 0.4109 | FALSE |
Ethnicity_CollapsedPrefer not to say | 0.4314 | 0.6492 | 1.5395 | -0.7254 | 1.8909 | 0.5063 | FALSE |
Ethnicity_CollapsedWhite Other | -0.1453 | 0.1543 | 0.8648 | -0.4388 | 0.1567 | 0.3462 | FALSE |
SexFemale | -0.2371 | 0.0688 | 0.7889 | -0.3719 | -0.1021 | 0.0006 | TRUE |
SexOther | -1.2128 | 1.5148 | 0.2973 | -4.5284 | 2.5177 | 0.4233 | FALSE |
Education_BandLow | -0.4324 | 0.1255 | 0.6490 | -0.6781 | -0.1815 | 0.0006 | TRUE |
Education_BandMid | -0.3036 | 0.0783 | 0.7382 | -0.4595 | -0.1469 | 0.0001 | TRUE |
BORNUK_binaryNot born in UK | 0.3230 | 0.0916 | 1.3813 | 0.1483 | 0.4994 | 0.0004 | TRUE |
BORNUK_binaryPrefer not to say | -0.8291 | 0.2663 | 0.4364 | -1.3420 | -0.2989 | 0.0018 | TRUE |
Age | -0.0058 | 0.0027 | 0.9942 | -0.0112 | -0.0004 | 0.0339 | TRUE |
RegionEast Midlands | -0.0311 | 0.1445 | 0.9694 | -0.3125 | 0.2551 | 0.8298 | FALSE |
RegionEast of England | 0.1038 | 0.1425 | 1.1094 | -0.1761 | 0.3884 | 0.4664 | FALSE |
RegionNorth East | -0.0869 | 0.1838 | 0.9168 | -0.4451 | 0.2834 | 0.6365 | FALSE |
RegionNorth West | -0.0799 | 0.1253 | 0.9232 | -0.3260 | 0.1673 | 0.5237 | FALSE |
RegionNorthern Ireland | 0.1957 | 0.2571 | 1.2161 | -0.2924 | 0.7212 | 0.4466 | FALSE |
RegionScotland | 0.0312 | 0.1563 | 1.0317 | -0.2739 | 0.3430 | 0.8418 | FALSE |
RegionSouth East | 0.1095 | 0.1232 | 1.1157 | -0.1339 | 0.3539 | 0.3740 | FALSE |
RegionSouth West | 0.0866 | 0.1461 | 1.0904 | -0.1997 | 0.3776 | 0.5534 | FALSE |
RegionWales | 0.0235 | 0.1844 | 1.0238 | -0.3374 | 0.3965 | 0.8985 | FALSE |
RegionWest Midlands | -0.0525 | 0.1400 | 0.9489 | -0.3246 | 0.2236 | 0.7076 | FALSE |
RegionYorkshire and the Humber | -0.2809 | 0.1539 | 0.7551 | -0.5837 | 0.0265 | 0.0679 | FALSE |
OutsourcedNonOLgroup 2 agency and long term | -0.0921 | 0.0972 | 0.9120 | -0.2819 | 0.1013 | 0.3434 | FALSE |
OutsourcedNonOLgroup 3 5 or 6 indicators and long term | 0.1262 | 0.1172 | 1.1345 | -0.1010 | 0.3611 | 0.2816 | FALSE |
Education emerges as the strongest predictor, with both mid-education (rate ratio = 0.738) and low-education workers (rate ratio = 0.649) experiencing significantly fewer negative outcomes than highly educated workers. This counterintuitive finding suggests that higher education may increase expectations or awareness of workplace disadvantages rather than protecting against them.
Place of birth shows significant effects, with workers not born in the UK experiencing 38% more negative outcomes (rate ratio = 1.38) compared to UK-born workers, while those preferring not to disclose birth status report 56% fewer negative outcomes (rate ratio = 0.436). Descriptive patterns show that workers not born in the UK have the highest mean negative outcomes (3.2), while those born in the UK average 2.33 negative outcomes.
Ethnicity shows some variation, with Black/African/Caribbean workers reporting 24% fewer negative outcomes than White British workers (rate ratio = 0.761).
Gender differences are evident, with female workers experiencing 21% fewer negative outcomes than male workers (rate ratio = 0.789). Age shows a protective effect, with each additional year associated with slightly fewer negative outcomes (rate ratio = 0.994).
Income effects are notable, with high-income workers experiencing 22% fewer negative outcomes than mid-income workers (rate ratio = 0.777). The predicted values plot demonstrates this income effect more clearly by holding all other demographic variables constant. Under these controlled conditions, the model predicts that high-income workers will experience approximately 2.14 negative outcomes compared to 2.75 for mid-income workers and 3.56 for low-income workers - a reduction of 0.61 and 1.42 (respectively) negative outcomes purely attributable to income level.
5.7 Work Conditions
Next we examine specific employment conditions that characterise outsourced work arrangements, focusing on five key dimensions: guaranteed hours, notice periods for working schedules, advance warning of shift cancellations, compensation for cancelled shifts, and sick pay provision. The analysis presents descriptive statistics for each condition and conducts cross-tabulations with demographic variables to identify potential disparities in work conditions across different groups. Statistical tests (chi-square or Fisher’s exact tests) are employed to assess the significance of observed associations, with effect sizes calculated using Cramér’s V.
Guaranteed_Hours :
1-8 hours 16-24 hours
129 183
25-35 hours 35+ hours
300 972
9-15 hours No guaranteed hours (zero hours)
121 109
Total responses: 1814
Notice_Of_Working_Hours :
1-2 weeks
238
1-3 days
282
4-6 days
318
4 weeks or more
183
Less than 24 hours
132
More than 2 weeks but less than 4 weeks
99
Not applicable – my job does not involve variable working hours or shift work
562
Total responses: 1814
Notice_Of_Cancelled_Shifts :
No
1054
Not applicable - my job does not involve variable working hours or shift work
353
Yes
407
Total responses: 1814
Cancelled_Shift_Pay :
0% 1-24% 100% 25-49% 50-74% 75-99% <NA>
92 104 44 80 50 37 1407
Total responses: 1814
Sick_Pay :
I don’t know / I’m not sure
114
No, I do not have access to sick pay if I am off work sick
263
Yes, my employer pays me sick pay which is above the basic rate of statutory sick pay (£116.75 a week) but below my usu
268
Yes, my employer pays my usual rate of pay for my usual working hours, so I receive my usual pay
918
Yes, my employer pays the basic rate of statutory sick pay (£116.75 a week)
251
Total responses: 1814
Work Condition Variable | Total Responses | Missing Values | Most Common Response | Percentage |
|---|---|---|---|---|
Guaranteed Hours | 1,814 | 0 | 35+ hours | 53.6% |
Notice of Working Hours | 1,814 | 0 | Not applicable – my job does not involve variable working hours or shift work | 31% |
Notice of Cancelled Shifts | 1,814 | 0 | No | 58.1% |
Cancelled Shift Pay | 407 | 1,407 | 1-24% | 25.6% |
Sick Pay | 1,814 | 0 | Yes, my employer pays my usual rate of pay for my usual working hours, so I receive my usual pay | 50.6% |
label | variable | income_group | Total | |||
|---|---|---|---|---|---|---|
Mid | High | Low | NA | |||
Notice_Of_Working_Hours_Simplified | 1-2 weeks | 111 (13.36%) | 52 (15.48%) | 46 (11.70%) | 5 | 214 (13.12%) |
4 weeks or more | 88 (10.59%) | 41 (12.20%) | 21 (5.34%) | 12 | 162 (9.93%) | |
Less than a week | 316 (38.03%) | 108 (32.14%) | 209 (53.18%) | 23 | 656 (40.22%) | |
More than 2 weeks but less than 4 weeks | 57 (6.86%) | 14 (4.17%) | 18 (4.58%) | 2 | 91 (5.58%) | |
Not applicable – my job does not involve variable working hours or shift work | 259 (31.17%) | 121 (36.01%) | 99 (25.19%) | 29 | 508 (31.15%) | |
Total | 831 (53.27%) | 336 (21.54%) | 393 (25.19%) | 71 | 1631 (100.00%) | |
label | variable | income_group | Total | |||
|---|---|---|---|---|---|---|
Mid | High | Low | NA | |||
Guaranteed_Hours | 1-8 hours | 48 (5.78%) | 12 (3.57%) | 41 (10.43%) | 7 | 108 (6.62%) |
16-24 hours | 48 (5.78%) | 14 (4.17%) | 103 (26.21%) | 13 | 178 (10.91%) | |
25-35 hours | 116 (13.96%) | 52 (15.48%) | 84 (21.37%) | 8 | 260 (15.94%) | |
35+ hours | 542 (65.22%) | 231 (68.75%) | 62 (15.78%) | 33 | 868 (53.22%) | |
9-15 hours | 41 (4.93%) | 14 (4.17%) | 56 (14.25%) | 3 | 114 (6.99%) | |
No guaranteed hours (zero hours) | 36 (4.33%) | 13 (3.87%) | 47 (11.96%) | 7 | 103 (6.32%) | |
Total | 831 (53.27%) | 336 (21.54%) | 393 (25.19%) | 71 | 1631 (100.00%) | |
label | variable | income_group | Total | |||
|---|---|---|---|---|---|---|
Mid | High | Low | NA | |||
Sick_Pay | I don’t know / I’m not sure | 41 (4.93%) | 12 (3.57%) | 39 (9.92%) | 18 | 110 (6.74%) |
No, I do not have access to sick pay if I am off work sick | 117 (14.08%) | 23 (6.85%) | 98 (24.94%) | 15 | 253 (15.51%) | |
Yes, my employer pays me sick pay which is above the basic rate of statutory sick pay (£116.75 a week) but below my usu | 125 (15.04%) | 56 (16.67%) | 50 (12.72%) | 5 | 236 (14.47%) | |
Yes, my employer pays my usual rate of pay for my usual working hours, so I receive my usual pay | 423 (50.90%) | 203 (60.42%) | 155 (39.44%) | 26 | 807 (49.48%) | |
Yes, my employer pays the basic rate of statutory sick pay (£116.75 a week) | 125 (15.04%) | 42 (12.50%) | 51 (12.98%) | 7 | 225 (13.80%) | |
Total | 831 (53.27%) | 336 (21.54%) | 393 (25.19%) | 71 | 1631 (100.00%) | |
label | variable | Ethnicity_Collapsed | Total | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
White British | Arab | Asian/Asian British | Black/African/Caribbean/Black British | Mixed/Multiple ethnic groups | Other ethnic group | Prefer not to say | White Other | NA | |||
Notice_Of_Working_Hours_Simplified | 1-2 weeks | 152 (12.93%) | 1 (11.11%) | 25 (14.79%) | 36 (15.32%) | 11 (12.09%) | 0 (0%) | 0 (0%) | 11 (10.38%) | 2 | 238 (13.12%) |
4 weeks or more | 135 (11.48%) | 1 (11.11%) | 17 (10.06%) | 16 (6.81%) | 5 (5.49%) | 0 (0%) | 0 (0%) | 7 (6.60%) | 2 | 183 (10.09%) | |
Less than a week | 419 (35.63%) | 5 (55.56%) | 76 (44.97%) | 130 (55.32%) | 42 (46.15%) | 2 (66.67%) | 1 (20.00%) | 48 (45.28%) | 9 | 732 (40.35%) | |
More than 2 weeks but less than 4 weeks | 66 (5.61%) | 0 (0%) | 7 (4.14%) | 9 (3.83%) | 7 (7.69%) | 1 (33.33%) | 0 (0%) | 7 (6.60%) | 2 | 99 (5.46%) | |
Not applicable – my job does not involve variable working hours or shift work | 404 (34.35%) | 2 (22.22%) | 44 (26.04%) | 44 (18.72%) | 26 (28.57%) | 0 (0%) | 4 (80.00%) | 33 (31.13%) | 5 | 562 (30.98%) | |
Total | 1176 (65.55%) | 9 (0.50%) | 169 (9.42%) | 235 (13.10%) | 91 (5.07%) | 3 (0.17%) | 5 (0.28%) | 106 (5.91%) | 20 | 1814 (100.00%) | |
Chi-square assumption check - Minimum expected frequency: 0.16
✗ Chi-square assumptions violated - using Fisher's exact test
Test p.value Cramers.V Effect.Size Significance
1 Fisher's exact test < 0.001 0.095 Negligible Significant
Post-hoc Analysis Assessment:
✓ Overall association is significant
✓ Multiple categories present - post-hoc pairwise comparisons recommended
Pairwise Fisher's Exact Tests (Bonferroni corrected):
Comparison
1 White British vs Mixed/Multiple ethnic groups
2 White British vs Black/African/Caribbean/Black British
3 White British vs White Other
4 White British vs Asian/Asian British
5 White British vs Arab
6 White British vs Other ethnic group
7 White British vs Prefer not to say
8 Mixed/Multiple ethnic groups vs Black/African/Caribbean/Black British
9 Mixed/Multiple ethnic groups vs White Other
10 Mixed/Multiple ethnic groups vs Asian/Asian British
11 Mixed/Multiple ethnic groups vs Arab
12 Mixed/Multiple ethnic groups vs Other ethnic group
13 Mixed/Multiple ethnic groups vs Prefer not to say
14 Black/African/Caribbean/Black British vs White Other
15 Black/African/Caribbean/Black British vs Asian/Asian British
16 Black/African/Caribbean/Black British vs Arab
17 Black/African/Caribbean/Black British vs Other ethnic group
18 Black/African/Caribbean/Black British vs Prefer not to say
19 White Other vs Asian/Asian British
20 White Other vs Arab
21 White Other vs Other ethnic group
22 White Other vs Prefer not to say
23 Asian/Asian British vs Arab
24 Asian/Asian British vs Other ethnic group
25 Asian/Asian British vs Prefer not to say
26 Arab vs Other ethnic group
27 Arab vs Prefer not to say
28 Other ethnic group vs Prefer not to say
p_value p_adjusted significant_adjusted cramers_v effect_size
1 0.1372 1.0000 FALSE 0.073 Negligible
2 0.0002 0.0056 TRUE 0.170 Small
3 0.2501 1.0000 FALSE 0.066 Negligible
4 0.1092 1.0000 FALSE 0.076 Negligible
5 0.8738 1.0000 FALSE 0.040 Negligible
6 0.1946 1.0000 FALSE 0.075 Negligible
7 0.5051 1.0000 FALSE 0.064 Negligible
8 0.1552 1.0000 FALSE 0.143 Small
9 0.9804 1.0000 FALSE 0.046 Negligible
10 0.5085 1.0000 FALSE 0.113 Small
11 0.8414 1.0000 FALSE 0.118 Small
12 0.3663 1.0000 FALSE 0.203 Small
13 0.3311 1.0000 FALSE 0.250 Small
14 0.0624 1.0000 FALSE 0.161 Small
15 0.2136 1.0000 FALSE 0.120 Small
16 0.8594 1.0000 FALSE 0.055 Negligible
17 0.2679 1.0000 FALSE 0.177 Small
18 0.0570 1.0000 FALSE 0.220 Small
19 0.5303 1.0000 FALSE 0.109 Small
20 0.9182 1.0000 FALSE 0.104 Small
21 0.3059 1.0000 FALSE 0.205 Small
22 0.4013 1.0000 FALSE 0.218 Small
23 1.0000 1.0000 FALSE 0.064 Negligible
24 0.2659 1.0000 FALSE 0.205 Small
25 0.2258 1.0000 FALSE 0.204 Small
26 0.6615 1.0000 FALSE 0.604 Large
27 0.2340 1.0000 FALSE 0.571 Large
28 0.0688 1.0000 FALSE 0.803 Large
less_than_week_direction
1 Mixed/Multiple ethnic groups higher (46.2% vs 35.6%)
2 Black/African/Caribbean/Black British higher (55.3% vs 35.6%)
3 White Other higher (45.3% vs 35.6%)
4 Asian/Asian British higher (45% vs 35.6%)
5 Arab higher (55.6% vs 35.6%)
6 Other ethnic group higher (66.7% vs 35.6%)
7 White British higher (35.6% vs 20%)
8 Black/African/Caribbean/Black British higher (55.3% vs 46.2%)
9 Mixed/Multiple ethnic groups higher (46.2% vs 45.3%)
10 Mixed/Multiple ethnic groups higher (46.2% vs 45%)
11 Arab higher (55.6% vs 46.2%)
12 Other ethnic group higher (66.7% vs 46.2%)
13 Mixed/Multiple ethnic groups higher (46.2% vs 20%)
14 Black/African/Caribbean/Black British higher (55.3% vs 45.3%)
15 Black/African/Caribbean/Black British higher (55.3% vs 45%)
16 Arab higher (55.6% vs 55.3%)
17 Other ethnic group higher (66.7% vs 55.3%)
18 Black/African/Caribbean/Black British higher (55.3% vs 20%)
19 White Other higher (45.3% vs 45%)
20 Arab higher (55.6% vs 45.3%)
21 Other ethnic group higher (66.7% vs 45.3%)
22 White Other higher (45.3% vs 20%)
23 Arab higher (55.6% vs 45%)
24 Other ethnic group higher (66.7% vs 45%)
25 Asian/Asian British higher (45% vs 20%)
26 Other ethnic group higher (66.7% vs 55.6%)
27 Arab higher (55.6% vs 20%)
28 Other ethnic group higher (66.7% vs 20%)
Significant pairwise differences (after Bonferroni correction):
• White British vs Black/African/Caribbean/Black British (p = 0.0056 , Cramér's V = 0.17 )
Direction: Black/African/Caribbean/Black British higher (55.3% vs 35.6%)
label | variable | Ethnicity_Collapsed | Total | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
White British | Arab | Asian/Asian British | Black/African/Caribbean/Black British | Mixed/Multiple ethnic groups | Other ethnic group | Prefer not to say | White Other | NA | |||
Guaranteed_Hours | 1-8 hours | 67 (5.70%) | 2 (22.22%) | 22 (13.02%) | 13 (5.53%) | 11 (12.09%) | 1 (33.33%) | 0 (0%) | 9 (8.49%) | 4 | 129 (7.11%) |
16-24 hours | 124 (10.54%) | 1 (11.11%) | 15 (8.88%) | 22 (9.36%) | 7 (7.69%) | 0 (0%) | 1 (20.00%) | 12 (11.32%) | 1 | 183 (10.09%) | |
25-35 hours | 183 (15.56%) | 2 (22.22%) | 21 (12.43%) | 60 (25.53%) | 13 (14.29%) | 1 (33.33%) | 1 (20.00%) | 17 (16.04%) | 2 | 300 (16.54%) | |
35+ hours | 651 (55.36%) | 3 (33.33%) | 90 (53.25%) | 109 (46.38%) | 52 (57.14%) | 1 (33.33%) | 3 (60.00%) | 52 (49.06%) | 11 | 972 (53.58%) | |
9-15 hours | 78 (6.63%) | 0 (0%) | 9 (5.33%) | 23 (9.79%) | 5 (5.49%) | 0 (0%) | 0 (0%) | 4 (3.77%) | 2 | 121 (6.67%) | |
No guaranteed hours (zero hours) | 73 (6.21%) | 1 (11.11%) | 12 (7.10%) | 8 (3.40%) | 3 (3.30%) | 0 (0%) | 0 (0%) | 12 (11.32%) | 0 | 109 (6.01%) | |
Total | 1176 (65.55%) | 9 (0.50%) | 169 (9.42%) | 235 (13.10%) | 91 (5.07%) | 3 (0.17%) | 5 (0.28%) | 106 (5.91%) | 20 | 1814 (100.00%) | |
Chi-square assumption check - Minimum expected frequency: 0.18
✗ Chi-square assumptions violated - using Fisher's exact test
White British Arab Asian/Asian British
1-8 hours 67 2 22
16-24 hours 124 1 15
25-35 hours 183 2 21
35+ hours 651 3 90
9-15 hours 78 0 9
No guaranteed hours (zero hours) 73 1 12
Black/African/Caribbean/Black British
1-8 hours 13
16-24 hours 22
25-35 hours 60
35+ hours 109
9-15 hours 23
No guaranteed hours (zero hours) 8
Mixed/Multiple ethnic groups
1-8 hours 11
16-24 hours 7
25-35 hours 13
35+ hours 52
9-15 hours 5
No guaranteed hours (zero hours) 3
Other ethnic group Prefer not to say
1-8 hours 1 0
16-24 hours 0 1
25-35 hours 1 1
35+ hours 1 3
9-15 hours 0 0
No guaranteed hours (zero hours) 0 0
White Other
1-8 hours 9
16-24 hours 12
25-35 hours 17
35+ hours 52
9-15 hours 4
No guaranteed hours (zero hours) 12
Fisher's Exact Test Results:
Test p.value Cramers.V Effect.Size Significance
1 Fisher's exact test 0.0022 0.082 Negligible Significant
Post-hoc Analysis Assessment:
✓ Overall association is significant
✓ Multiple categories present - post-hoc pairwise comparisons recommended
Pairwise Fisher's Exact Tests (Bonferroni corrected):
Comparison
1 White British vs Mixed/Multiple ethnic groups
2 White British vs Black/African/Caribbean/Black British
3 White British vs White Other
4 White British vs Asian/Asian British
5 White British vs Arab
6 White British vs Other ethnic group
7 White British vs Prefer not to say
8 Mixed/Multiple ethnic groups vs Black/African/Caribbean/Black British
9 Mixed/Multiple ethnic groups vs White Other
10 Mixed/Multiple ethnic groups vs Asian/Asian British
11 Mixed/Multiple ethnic groups vs Arab
12 Mixed/Multiple ethnic groups vs Other ethnic group
13 Mixed/Multiple ethnic groups vs Prefer not to say
14 Black/African/Caribbean/Black British vs White Other
15 Black/African/Caribbean/Black British vs Asian/Asian British
16 Black/African/Caribbean/Black British vs Arab
17 Black/African/Caribbean/Black British vs Other ethnic group
18 Black/African/Caribbean/Black British vs Prefer not to say
19 White Other vs Asian/Asian British
20 White Other vs Arab
21 White Other vs Other ethnic group
22 White Other vs Prefer not to say
23 Asian/Asian British vs Arab
24 Asian/Asian British vs Other ethnic group
25 Asian/Asian British vs Prefer not to say
26 Arab vs Other ethnic group
27 Arab vs Prefer not to say
28 Other ethnic group vs Prefer not to say
p_value p_adjusted significant_adjusted cramers_v effect_size
1 0.2446 1.0000 FALSE 0.078 Negligible
2 0.0024 0.0672 FALSE 0.119 Small
3 0.2198 1.0000 FALSE 0.075 Negligible
4 0.0348 0.9742 FALSE 0.102 Small
5 0.1588 1.0000 FALSE 0.072 Negligible
6 0.2697 1.0000 FALSE 0.068 Negligible
7 0.9340 1.0000 FALSE 0.035 Negligible
8 0.0520 1.0000 FALSE 0.181 Small
9 0.2589 1.0000 FALSE 0.181 Small
10 0.8752 1.0000 FALSE 0.086 Negligible
11 0.3157 1.0000 FALSE 0.192 Small
12 0.4755 1.0000 FALSE 0.164 Small
13 0.7427 1.0000 FALSE 0.145 Small
14 0.0082 0.2296 FALSE 0.213 Small
15 0.0014 0.0392 TRUE 0.228 Small
16 0.1866 1.0000 FALSE 0.164 Small
17 0.3967 1.0000 FALSE 0.141 Small
18 0.8804 1.0000 FALSE 0.086 Negligible
19 0.5505 1.0000 FALSE 0.122 Small
20 0.6467 1.0000 FALSE 0.150 Small
21 0.4343 1.0000 FALSE 0.177 Small
22 1.0000 1.0000 FALSE 0.122 Small
23 0.5055 1.0000 FALSE 0.119 Small
24 0.5111 1.0000 FALSE 0.127 Small
25 0.7600 1.0000 FALSE 0.112 Small
26 1.0000 1.0000 FALSE 0.272 Small
27 0.9096 1.0000 FALSE 0.413 Medium
28 0.8012 1.0000 FALSE 0.577 Large
hours_35plus_direction
1 Mixed/Multiple ethnic groups higher 35+ hours (57.1% vs 55.4%)
2 White British higher 35+ hours (55.4% vs 46.4%)
3 White British higher 35+ hours (55.4% vs 49.1%)
4 White British higher 35+ hours (55.4% vs 53.3%)
5 White British higher 35+ hours (55.4% vs 33.3%)
6 White British higher 35+ hours (55.4% vs 33.3%)
7 Prefer not to say higher 35+ hours (60% vs 55.4%)
8 Mixed/Multiple ethnic groups higher 35+ hours (57.1% vs 46.4%)
9 Mixed/Multiple ethnic groups higher 35+ hours (57.1% vs 49.1%)
10 Mixed/Multiple ethnic groups higher 35+ hours (57.1% vs 53.3%)
11 Mixed/Multiple ethnic groups higher 35+ hours (57.1% vs 33.3%)
12 Mixed/Multiple ethnic groups higher 35+ hours (57.1% vs 33.3%)
13 Prefer not to say higher 35+ hours (60% vs 57.1%)
14 White Other higher 35+ hours (49.1% vs 46.4%)
15 Asian/Asian British higher 35+ hours (53.3% vs 46.4%)
16 Black/African/Caribbean/Black British higher 35+ hours (46.4% vs 33.3%)
17 Black/African/Caribbean/Black British higher 35+ hours (46.4% vs 33.3%)
18 Prefer not to say higher 35+ hours (60% vs 46.4%)
19 Asian/Asian British higher 35+ hours (53.3% vs 49.1%)
20 White Other higher 35+ hours (49.1% vs 33.3%)
21 White Other higher 35+ hours (49.1% vs 33.3%)
22 Prefer not to say higher 35+ hours (60% vs 49.1%)
23 Asian/Asian British higher 35+ hours (53.3% vs 33.3%)
24 Asian/Asian British higher 35+ hours (53.3% vs 33.3%)
25 Prefer not to say higher 35+ hours (60% vs 53.3%)
26 Similar 35+ hours (33.3% vs 33.3%)
27 Prefer not to say higher 35+ hours (60% vs 33.3%)
28 Prefer not to say higher 35+ hours (60% vs 33.3%)
Significant pairwise differences (after Bonferroni correction):
• Black/African/Caribbean/Black British vs Asian/Asian British (p = 0.0392 , Cramér's V = 0.228 )
Direction: Asian/Asian British higher 35+ hours (53.3% vs 46.4%)
The work conditions analysis reveals varying degrees of job security and workplace protections among outsourced workers. Hour guarantees show moderate security, with 53.6% having 35+ hour contracts, though 6.0% work zero-hours contracts and 23.9% have part-time guarantees (1-24 hours). Income disparities are stark in hour guarantees: low-income workers face zero-hours contracts at triple the rate (12.0%) compared to high-income (3.7%) and mid-income workers (4.3%), while only 15.8% of low-income workers have 35+ hour guarantees versus 64.5% of high-income workers.
Scheduling predictability presents challenges, with 40.3% of workers receiving less than one week’s notice of working hours, while 28.7% receive one week or more notice. Shift cancellations affect a significant minority, with 22.4% experiencing cancelled shifts. Compensation for cancelled shifts shows concerning patterns: among those experiencing cancellations, 22.6% receive no pay, 45.3% receive partial compensation (1-49%), and only 32.2% receive most or full pay (50-100%).
Sick pay provision is mixed, with half (50.6%) receiving full usual pay when sick, but 14.5% having no sick pay access and 13.8% limited to statutory minimum (£116.75/week). Uncertainty about entitlements affects 6.3% who don’t know their sick pay rights.
The overall pattern suggests a two-tier system where higher-income outsourced workers enjoy more stable conditions (guaranteed hours, better scheduling predictability) while lower-income workers face greater insecurity through zero-hours contracts and unpredictable scheduling. This stratification within outsourced work creates differential vulnerabilities across income groups.
5.8 Rights Violations
This analysis examines reported workplace rights violations among outsourced workers, focusing on the prevalence and nature of violations across different categories. The analysis employs descriptive statistics to document the frequency of various rights violations and explores demographic correlates of violation experiences. Cross-tabulations with key variables (income group, ethnicity, employment type) are conducted to identify vulnerable populations, with statistical significance assessed through appropriate tests and effect sizes calculated where applicable.
Rights Violation | Count | Percentage (%) |
|---|---|---|
Not being paid on time | 199 | 11 |
Not being given time off that I am entitled to | 203 | 11 |
Not being given pay that I am entitled to while being off sick | 197 | 11 |
Not having adequate health and safety protections | 178 | 10 |
Not being paid the full amount I am entitled to for the work I have completed | 167 | 9 |
Not being paid for paid leave that I am entitled to | 158 | 9 |
Not being provided with a pay slip | 164 | 9 |
The rights violations analysis reveals that nearly half of outsourced workers experience at least one workplace rights violation, with 56.3% reporting no violations. Leave entitlement issues are the most prevalent violation (11.2%), followed closely by payment timing problems (11.0%) and sick pay violations (10.9%). Health and safety protections are inadequate for 9.8% of workers, while pay slip provision (9.0%) and correct payment amounts (9.2%) also represent concerns.
5.9 Discrimination Analysis
This analysis examines reported experiences of discrimination among outsourced workers across multiple dimensions including age, disability, ethnicity, nationality, religion, and sex. The analysis employs descriptive statistics to document the prevalence of different types of discrimination and conducts cross-tabulations with demographic variables to identify patterns and vulnerable populations. Statistical tests assess the significance of observed associations, with particular attention to intersectional effects where multiple forms of discrimination may compound disadvantage among specific groups.
5.9.1 Sex-based Discrimination
=== SEX-BASED DISCRIMINATION ANALYSIS ===
Total valid responses for Inhouse_Discrimination_Sex : 1727
The sex-based discrimination analysis reveals significant variation in discrimination experiences across demographic groups, with Black workers reporting the highest rates of sex-based discrimination (42.9% experienced discrimination), followed by workers born outside the UK (41.9%). Asian workers also face elevated rates (38.2%), while female workers report discrimination at 31.3%. The overall rate among all outsourced workers is 30.8%. Notably, Black workers show the lowest percentage reporting “Never” experiencing discrimination (57%), compared to the overall average of 69%, suggesting more pervasive experiences of sex-based discrimination within this group. The pattern indicates that ethnicity and migration status intersect with gender to create heightened vulnerability to discriminatory treatment.
5.9.2 Ethnicity-based Discrimination
=== ETHNICITY-BASED DISCRIMINATION ANALYSIS ===
Total valid responses for Inhouse_Discrimination_Ethnicity : 1717
The ethnicity-based discrimination analysis reveals the highest discrimination rates among all forms examined, with Black workers and workers born outside the UK both experiencing discrimination at 52.5% and 52.3% respectively. Asian workers also face substantial discrimination (45.6%), while the overall rate among all outsourced workers is 30.0%. The data shows particularly concerning patterns for Black workers, with only 48% reporting “Never” experiencing ethnicity-based discrimination, compared to 70% overall. Similarly, workers born outside the UK show only 48% reporting “Never” experiencing discrimination. Notably, Black workers report the highest rates of frequent discrimination, with 24% experiencing it “Sometimes” and 10% “Often”. This pattern suggests that ethnicity-based discrimination is the most pervasive form of discrimination faced by outsourced workers, with Black workers and migrants bearing the heaviest burden.
5.9.3 Age-based Discrimination
=== AGE-BASED DISCRIMINATION ANALYSIS ===
Total valid responses for Inhouse_Discrimination_Age : 1730
The age-based discrimination analysis shows moderate discrimination rates across demographic groups, with Black workers experiencing the highest rates (44.9%), followed by workers born outside the UK (44.1%). Asian workers report discrimination at 35.8%, while the overall rate among all outsourced workers is 35.2%. Female workers experience age-based discrimination at 34.6%. The data reveals that Black workers have the lowest percentage reporting “Never” experiencing discrimination (55%), compared to 65% overall. Workers born outside the UK also show elevated vulnerability with 56% reporting “Never” experiencing discrimination. Age-based discrimination appears to be the second most common form of discrimination after ethnicity-based discrimination, with Black workers and migrants again showing heightened exposure to discriminatory treatment.
5.9.4 Disability-based Discrimination
=== DISABILITY-BASED DISCRIMINATION ANALYSIS ===
Total valid responses for Inhouse_Discrimination_Disability : 1713
The disability-based discrimination analysis shows lower overall discrimination rates compared to other forms, with workers born outside the UK experiencing the highest rates (31.9%), followed by Asian workers (27.5%) and Black workers (25.1%). The overall rate among all outsourced workers is 22.5%, while female workers report the lowest rate at 20.0%. The data shows that workers born outside the UK have the lowest percentage reporting “Never” experiencing discrimination (68%), compared to 77% overall. Notably, Asian workers show relatively high rates of frequent discrimination, with 14% experiencing it “Sometimes” and 6% “Often”. While disability-based discrimination is the least prevalent form among those examined, it still affects nearly one in four outsourced workers overall, with migrant workers showing particular vulnerability.
5.9.5 Nationality-based Discrimination
=== NATIONALITY-BASED DISCRIMINATION ANALYSIS ===
Total valid responses for Inhouse_Discrimination_Nationality : 1726
The nationality-based discrimination analysis reveals high discrimination rates that mirror the ethnicity-based patterns, with Black workers and workers born outside the UK both experiencing discrimination at 52.5% and 52.4% respectively. Asian workers face substantial discrimination at 43.1%, while the overall rate among all outsourced workers is 31.0%. Female workers report discrimination at 28.7%. The data shows that Black workers and workers born outside the UK both have only 48% reporting “Never” experiencing discrimination, compared to 69% overall. Black workers and workers born outside the UK also show the highest rates of frequent discrimination, with Black workers experiencing it “Sometimes” (24%) and “Often” (7%), while workers born outside the UK report similar patterns (21% “Sometimes”, 8% “Often”). The pattern confirms that nationality-based discrimination closely parallels ethnicity-based discrimination, suggesting these forms of discrimination are interconnected and particularly target migrant and ethnic minority workers.
5.10 Clarity Questions Analysis
We also explored workplace clarity among outsourced workers across eleven key dimensions including clarity about reporting structures for pay problems, rights and entitlements, time-off approval processes, promotion pathways, and role responsibilities. The analysis also assesses communication effectiveness between organisations, workers’ confidence in raising workplace improvements, and management responsiveness to discrimination, bullying, and racism complaints. Descriptive statistics and cross-tabulations with demographic variables identify patterns in workplace clarity and potential disparities in organisational transparency across different groups of outsourced workers.
Clarity Questions Summary Table:
Question Area | Total Responses | Strongly Agree (%) | Somewhat Agree (%) | Neither (%) | Somewhat Disagree (%) | Strongly Disagree (%) |
|---|---|---|---|---|---|---|
Role responsibilities | 1,814 | 48.2 | 31.1 | 13.6 | 4.7 | 2.3 |
Management handles racism | 1,814 | 44.4 | 28.8 | 17.3 | 5.7 | 3.8 |
Pay problems contact | 1,814 | 40.6 | 33.1 | 16.8 | 6.4 | 3.1 |
Time off approval | 1,814 | 40.6 | 34.1 | 16.3 | 6.3 | 2.7 |
Management handles bullying | 1,814 | 40.5 | 28.1 | 19.5 | 7.5 | 4.4 |
Rights/entitlements contact | 1,814 | 37.4 | 34.1 | 17.4 | 7.6 | 3.5 |
Management prevents discrimination | 1,814 | 37.0 | 30.5 | 20.8 | 8.1 | 3.6 |
Organisational communication | 1,814 | 36.1 | 33.5 | 19.7 | 7.1 | 3.6 |
Promotion contact | 1,814 | 35.3 | 32.0 | 19.7 | 7.6 | 5.3 |
Can suggest improvements | 1,814 | 34.2 | 32.9 | 21.5 | 8.1 | 3.2 |
Opinion will be respected | 1,814 | 31.4 | 34.5 | 22.3 | 7.3 | 4.5 |
=== CLARITY QUESTIONS BY INCOME GROUP ===
Clarity by Income Group Summary:
Income Group | Question Area | Total | Agree (%) | Disagree (%) | Neither (%) |
|---|---|---|---|---|---|
High | Can suggest improvements | 336 | 67.9 | 11.6 | 18.1 |
Mid | Can suggest improvements | 831 | 65.8 | 11.0 | 22.1 |
Low | Can suggest improvements | 393 | 60.3 | 12.7 | 24.1 |
High | Management handles bullying | 336 | 71.4 | 6.2 | 17.7 |
Mid | Management handles bullying | 831 | 65.2 | 13.1 | 18.9 |
Low | Management handles bullying | 393 | 54.5 | 13.7 | 25.3 |
High | Management handles racism | 336 | 74.7 | 6.5 | 13.1 |
Mid | Management handles racism | 831 | 69.3 | 11.0 | 16.0 |
Low | Management handles racism | 393 | 59.0 | 8.7 | 24.2 |
High | Management prevents discrimination | 336 | 73.5 | 6.2 | 17.8 |
Mid | Management prevents discrimination | 831 | 63.7 | 13.2 | 20.0 |
Low | Management prevents discrimination | 393 | 55.0 | 13.2 | 26.2 |
High | Opinion will be respected | 336 | 72.6 | 8.0 | 17.6 |
Mid | Opinion will be respected | 831 | 63.3 | 12.6 | 22.8 |
Low | Opinion will be respected | 393 | 55.5 | 13.7 | 27.1 |
High | Organisational communication | 336 | 74.4 | 9.8 | 13.5 |
Mid | Organisational communication | 831 | 66.4 | 10.6 | 21.6 |
Low | Organisational communication | 393 | 59.3 | 12.5 | 25.4 |
High | Pay problems contact | 336 | 74.7 | 6.8 | 15.7 |
Mid | Pay problems contact | 831 | 70.8 | 10.7 | 17.0 |
Low | Pay problems contact | 393 | 67.7 | 11.2 | 18.0 |
High | Promotion contact | 336 | 72.0 | 8.9 | 17.3 |
Mid | Promotion contact | 831 | 63.8 | 14.6 | 19.2 |
Low | Promotion contact | 393 | 56.5 | 15.3 | 23.0 |
High | Rights/entitlements contact | 336 | 72.9 | 10.1 | 14.9 |
Mid | Rights/entitlements contact | 831 | 69.9 | 11.7 | 17.1 |
Low | Rights/entitlements contact | 393 | 61.6 | 12.0 | 23.3 |
High | Role responsibilities | 336 | 80.1 | 6.8 | 10.7 |
Mid | Role responsibilities | 831 | 77.3 | 7.2 | 14.5 |
Low | Role responsibilities | 393 | 74.6 | 7.9 | 14.5 |
High | Time off approval | 336 | 77.1 | 8.9 | 11.6 |
Mid | Time off approval | 831 | 71.7 | 9.3 | 17.2 |
Low | Time off approval | 393 | 70.7 | 8.7 | 18.1 |
Statistically Significant Results (p < 0.1):
Factor | Effect | 95% CI Lower | 95% CI Upper | Sig. | P-value |
|---|---|---|---|---|---|
Age (per year) | 0.007 | 0.004 | 0.010 | *** | <0.001 |
Not born in UK (vs Born in UK) | -0.239 | -0.358 | -0.120 | *** | <0.001 |
Black/African/Caribbean (vs White British) | 0.310 | 0.116 | 0.504 | ** | 0.002 |
High income (vs Mid income) | 0.166 | 0.054 | 0.278 | ** | 0.004 |
Low income (vs Mid income) | -0.148 | -0.249 | -0.047 | ** | 0.004 |
Region: Wales | 0.263 | 0.060 | 0.466 | * | 0.011 |
Region: Yorkshire and the Humber | 0.225 | 0.042 | 0.408 | * | 0.016 |
White Other (vs White British) | 0.214 | 0.022 | 0.406 | * | 0.029 |
Region: North East | 0.284 | 0.024 | 0.543 | * | 0.032 |
Region: Northern Ireland | -0.279 | -0.556 | -0.003 | * | 0.048 |
The clarity analysis reveals significant variation in workplace transparency across different domains and income groups. Role responsibilities show the highest clarity (77.5% agreement), followed by time off approval (72.8%) and pay problems contact (71.6%), indicating that basic operational processes are generally well-understood. However, areas requiring higher-level organisational engagement show concerning gaps: opinion will be respected (64.1%), management prevents discrimination (64.4%), and promotion contact (64.7%) have the lowest agreement rates.
Income-based disparities are particularly stark, with high-income workers consistently reporting greater clarity than low-income workers across all domains. The largest gaps appear in management prevents discrimination (18.5 percentage point difference), opinion will be respected (17.1 points), and management handles bullying (16.9 points). These patterns suggest that organisational transparency and confidence in management responsiveness decrease substantially as income levels fall, potentially reflecting differential treatment or reduced organisational investment in communication with lower-paid outsourced workers. The consistency of these income-based disparities across multiple domains points to systemic inequalities in workplace clarity rather than isolated communication issues.
5.11 Work Preference by Income Group
Finally we examine the relationship between income levels and work preferences among outsourced workers, comparing preferences for in-house versus outsourced employment across low, mid, and high income groups. The analysis employs cross-tabulations and statistical tests to identify whether income level influences workers’ preferences for employment arrangements, with particular attention to understanding how economic circumstances may shape attitudes toward outsourcing.
Work Preference by Income Group Cross-tabulation:
label | variable | income_group | Total | ||
|---|---|---|---|---|---|
Mid Income | High Income | Low Income | |||
Work_Preference | I have no preference | 342 (41.2%) | 140 (41.7%) | 162 (41.2%) | 644 (41.3%) |
I would prefer to be an in-house worker | 145 (17.4%) | 57 (17.0%) | 56 (14.2%) | 258 (16.5%) | |
I would prefer to be an outsourced worker | 90 (10.8%) | 33 (9.8%) | 38 (9.7%) | 161 (10.3%) | |
I would strongly prefer to be an in-house worker | 130 (15.6%) | 61 (18.2%) | 52 (13.2%) | 243 (15.6%) | |
I would strongly prefer to be an outsourced worker | 54 (6.5%) | 27 (8.0%) | 20 (5.1%) | 101 (6.5%) | |
Not sure | 70 (8.4%) | 18 (5.4%) | 65 (16.5%) | 153 (9.8%) | |
Total | 831 (53.3%) | 336 (21.5%) | 393 (25.2%) | 1560 (100.0%) | |
=== Statistical Analysis: Work Preference by Income Group ===
Statistical Test Results: Work Preference by Income Group | |
|---|---|
Statistic | Value |
Test Method | Fisher's Exact Test (simulated) |
Sample Size | 1560 |
P-value | < 0.001 |
Significance | *** |
Cramér's V | 0.104 |
Effect Size | Small |
Method Details | Monte Carlo simulation with 10,000 replicates |
=== Summary Statistics ===
Key findings:
- Low income: 27.4% prefer in-house, 14.8% prefer outsourced
- Mid income: 33.0% prefer in-house, 17.3% prefer outsourced
- High income: 35.2% prefer in-house, 17.8% prefer outsourced
The work preference analysis reveals clear patterns in employment arrangement preferences across income groups, with statistical significance confirmed by Fisher’s exact test (p < 0.001, Cramér’s V = 0.104, small effect size). No preference dominates across all income groups (approximately 41%), but meaningful differences emerge in definitive preferences and uncertainty levels.
Income-based preference patterns show that higher-income workers increasingly prefer in-house employment: low-income workers prefer in-house work at 27.4%, rising to 33.0% for mid-income and 35.2% for high-income workers. Conversely, outsourced work preferences remain relatively stable across income groups (14.8% to 17.8%), suggesting that income level primarily influences attraction to in-house employment rather than satisfaction with outsourcing.
The most striking pattern appears in uncertainty levels, where low-income workers show dramatically higher “not sure” responses (16.5%) compared to mid-income (8.4%) and high-income workers (5.4%). This 11.1 percentage point gap between low and high-income groups suggests that economic insecurity may contribute to greater uncertainty about employment preferences, potentially reflecting limited exposure to alternative employment arrangements or anxiety about job security that makes definitive preferences more difficult to form. The consistent 2:1 ratio favoring in-house work across all income groups indicates that while income influences preference strength, the fundamental appeal of in-house employment remains broadly consistent.
6 Limitations and Future Research?
7 Reproducibility
All analyses presented in this report can be fully reproduced using the code and data provided in the Just Knowlegde GitHub repository.
8 Appendices
8.1 Study 1 - Age
The table below shows weighted descriptive statistics of the sample, and the figure below shows the frequency of respondents at each single year of age.
| Mean | Median | Min | Max | Standard dev. |
|---|---|---|---|---|
| 42.1 | 42 | 16 | 80 | 13.17 |
8.2 Study 1 - Gender
The table below shows the weighted gender breakdown of the sample
| Gender | Weighted frequency | Weighted percentage |
|---|---|---|
| Male | 4957.18 | 48.82 |
| Female | 5117.61 | 50.39 |
| Other | 15.37 | 0.15 |
| Prefer not to say | 64.84 | 0.64 |
8.3 Study 1 - Ethnicity
The table below shows the weighted ethnicity breakdown using the full range of Census 2021 categories. Note that ‘NA’ indicates non-responses.
| Ethnicity | Weighted frequency | Weighted percentage |
|---|---|---|
| English / Welsh / Scottish / Northern Irish / British | 7732.24 | 76.14 |
| Irish | 113.61 | 1.12 |
| Gypsy or Irish Traveller | 10.79 | 0.11 |
| Roma | 7.49 | 0.07 |
| Any other White background | 479.38 | 4.72 |
| White and Black Caribbean | 58.75 | 0.58 |
| White and Black African | 35.04 | 0.35 |
| White and Asian | 41.52 | 0.41 |
| Any other Mixed / Multiple ethnic background | 49.49 | 0.49 |
| Indian | 311.73 | 3.07 |
| Pakistani | 149.94 | 1.48 |
| Bangladeshi | 76.50 | 0.75 |
| Chinese | 145.53 | 1.43 |
| Any other Asian background | 163.15 | 1.61 |
| African | 227.05 | 2.24 |
| Caribbean | 71.67 | 0.71 |
| Any other Black, Black British, or Caribbean background | 37.39 | 0.37 |
| Arab | 32.50 | 0.32 |
| Any other ethnic group | 30.46 | 0.30 |
| Don’t think of myself as any of these | 8.81 | 0.09 |
| Prefer not to say | 30.45 | 0.30 |
| NA | 341.51 | 3.36 |
We also make use of an aggregated ethnicity variable that groups ethnicities into fewer categories. The table below shows how the Census categories map onto the aggregated categories.
| Census categories | Aggregated categories |
|---|---|
| English / Welsh / Scottish / Northern Irish / British | White British |
| Irish | White other |
| Gypsy or Irish Traveller | White other |
| Roma | White other |
| Any other White background | White other |
| White and Black Caribbean | Mixed/Multiple ethnic group |
| White and Black African | Mixed/Multiple ethnic group |
| White and Asian | Mixed/Multiple ethnic group |
| Any other Mixed / Multiple ethnic background | Mixed/Multiple ethnic group |
| Indian | Asian/Asian British |
| Pakistani | Asian/Asian British |
| Bangladeshi | Asian/Asian British |
| Chinese | Asian/Asian British |
| Any other Asian background | Asian/Asian British |
| African | Black/African/Caribbean/Black British |
| Caribbean | Black/African/Caribbean/Black British |
| Any other Black, Black British, or Caribbean background | Black/African/Caribbean/Black British |
| Arab | Arab/British Arab |
| Any other ethnic group | Other ethnic group |
| Don’t think of myself as any of these | Don't think of myself as any of these |
| Prefer not to say | Prefer not to say |
| NA | NA |
The table below shows the weighted ethnicity breakdown using the aggregated set of categories
| Ethnicity | Weighted frequency | Weighted percentage |
|---|---|---|
| White British | 7732.24 | 76.14 |
| Arab/British Arab | 32.50 | 0.32 |
| Asian/Asian British | 846.86 | 8.34 |
| Black/African/Caribbean/Black British | 336.10 | 3.31 |
| Don't think of myself as any of these | 8.81 | 0.09 |
| Mixed/Multiple ethnic group | 184.80 | 1.82 |
| Other ethnic group | 30.46 | 0.30 |
| Prefer not to say | 30.45 | 0.30 |
| White other | 611.27 | 6.02 |
| NA | 341.51 | 3.36 |